R Notebook
#Introduction I am analyzing neogene expression in Patient sarcoma data.
#Load libraries
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:plyr’:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following object is masked from ‘package:Biobase’:
combine
The following objects are masked from ‘package:GenomicRanges’:
intersect, setdiff, union
The following object is masked from ‘package:GenomeInfoDb’:
intersect
The following objects are masked from ‘package:IRanges’:
collapse, desc, intersect, setdiff, slice, union
The following objects are masked from ‘package:S4Vectors’:
first, intersect, rename, setdiff, setequal, union
The following objects are masked from ‘package:BiocGenerics’:
combine, intersect, setdiff, union
The following object is masked from ‘package:matrixStats’:
count
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union#importing data from file storage
Files are stored on my MD Anderson drive. I am extracting the the path to each library and generating some meta data.
```r
list.dirs('/Volumes/ludwig_lab/CellRanger/data_output/', recursive = F)
list_of_files <- path_input('/Volumes/ludwig_lab/CellRanger/data_output/', library.split = 7, sample.split = 1)
list_of_files <- list_of_files[c(3,5,7,10),]
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#converting from H5 to seurat objects
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```r
```r
Patient_Sarcoma_neogenes.list.h5 <- lapply(list_of_files$h5, Read10X_h5)
names(Patient_Sarcoma_neogenes.list.h5) <- list_of_files$library_id
Patient_Sarcoma_neogenes.list <- lapply( Patient_Sarcoma_neogenes.list.h5 ,function(x) {CreateSeuratObject(counts = x) })
names(Patient_Sarcoma_neogenes.list) <- list_of_files$library_id
for(i in 1:length(Patient_Sarcoma_neogenes.list)) {
Patient_Sarcoma_neogenes.list[[i]]<- RenameCells(object = Patient_Sarcoma_neogenes.list[[i]], new.names = paste0(
sapply(strsplit(as.character(colnames(Patient_Sarcoma_neogenes.list[[i]])), split=\-\), \[[\, 1),
\-\, i))
}
Patient_Sarcoma_data <- Reduce(merge, Patient_Sarcoma_neogenes.list)
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#adding the metadata
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```r
```r
Patient_Sarcoma_data <- PercentageFeatureSet( Patient_Sarcoma_data, pattern = \^MT-\, col.name = \percent.mt\, assay = 'RNA')
gemgroup <- sapply(strsplit(rownames(Patient_Sarcoma_data@meta.data), split=\-\), \[[\, 2)
Patient_Sarcoma_data<- AddMetaData(object=Patient_Sarcoma_data, metadata=data.frame(gemgroup=gemgroup, row.names=rownames(Patient_Sarcoma_data@meta.data)))
Patient_Sarcoma_data$orig.ident <- mapvalues(Patient_Sarcoma_data$gemgroup,unique(Patient_Sarcoma_data$gemgroup), as.character(list_of_files$library_id))
Patient_Sarcoma_data$sample_type <- mapvalues(Patient_Sarcoma_data$gemgroup, unique(Patient_Sarcoma_data$gemgroup), as.character(list_of_files$sample_type))
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```r
```r
Patient_Sarcoma_data$lab_id <- mapvalues(Patient_Sarcoma_data$orig.ident, unique(Patient_Sarcoma_data$orig.ident), c('DSRCT-1', 'DSRCT-4', 'DSRCT-2',
'CDS-1' ))
Patient_Sarcoma_data@meta.data %>% group_by(orig.ident, lab_id) %>% summarise(n=n())
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#quality control
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```r
```r
RidgePlot(Patient_Sarcoma_data, features = 'nCount_RNA', group.by = 'lab_id') + scale_x_log10()
RidgePlot(Patient_Sarcoma_data, features = 'nFeature_RNA', group.by = 'lab_id')+ scale_x_log10()
RidgePlot(Patient_Sarcoma_data, features = 'percent.mt', group.by = 'lab_id')
Patient_Sarcoma_data
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```r
```r
Patient_Sarcoma_data <- readRDS('/Volumes/ludwig_lab/NeoGenes/RDS/Patient_Sarcoma_dataoriginal.rds')
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#filter out low quality cells
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```r
Patient_Sarcoma_data_filtered <- subset(Patient_Sarcoma_data,
subset =
percent.mt < 10 & nCount_RNA > 1000 & nFeature_RNA > 500)
Patient_Sarcoma_dataAn object of class Seurat
37002 features across 46879 samples within 1 assay
Active assay: RNA (37002 features, 0 variable features)Patient_Sarcoma_data_filteredAn object of class Seurat
37002 features across 44673 samples within 1 assay
Active assay: RNA (37002 features, 0 variable features)#save original data
```r
saveRDS(Patient_Sarcoma_data, '/Volumes/ludwig_lab/NeoGenes/RDS/Patient_Sarcoma_dataoriginal.rds')
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#Processing (including CDS)
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```r
Patient_Sarcoma_data <- readRDS('/Volumes/ludwig_lab/NeoGenes/RDS/Patient_Sarcoma_dataoriginal.rds')##normalize
Patient_Sarcoma_data_filtered <- Patient_Sarcoma_data_filtered %>%
NormalizeData() %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA()Performing log-normalization
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Calculating gene variances
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Calculating feature variances of standardized and clipped values
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Centering and scaling data matrix
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PC_ 1
Positive: ANK3, STXBP6, AC104041.1, MAP7, GRHL2, PLK5, ALCAM, AR, THSD7A, AGBL1
CSF2RA, ERBB4, FMN2, PTPRM, AL033523.1, DSRCT-NG18, MLPH, AL583808.1, AL117329.1, FIGN
GALNT17, AC013652.1, AP000829.1, ADAMTSL5, PCDH11X, CNTN3, PRKAR2B, COLEC12, DSRCT-NG31, SMOC2
Negative: COL6A3, ITIH5, CACNA2D1, NKAIN3, PVT1, NLGN1, CASC15, FAM20C, ADAMTSL1, CELF4
ZBTB7C, DPYSL3, PLEKHG4B, THSD7B, COL4A2, ST6GAL1, ANO4, AC068587.4, SMARCD3, TENM3
DST, ZNF385B, GRID1, DCLK2, DPF3, KCNAB2, AC009518.1, MATN2, MEF2C, ELAVL2
PC_ 2
Positive: PDZRN3, TMEM132C, PRKG1, IGF2BP3, EPB41L2, NIBAN1, PIK3C2B, AC087564.1, ELL2, NETO1
PCDH15, AC022915.2, ANKRD33B, ADARB2, GFRA1, DCDC2C, SASH1, EGR1, LAMA2, TPRG1
DMD, SGCD, BACH2, CNKSR3, ADGRG2, GPC6, ETV1, RGS6, MGAT4C, AC024022.1
Negative: CCSER1, COL24A1, GRB10, MATN2, SDK1, PLEKHG4B, PCBP3, PGBD5, PDE3A, DPF3
CASC15, DISP3, LINC01876, ZNF385B, AC078845.1, AC009518.1, ADAMTSL2, SMARCD3, THSD7B, CELF4
LINC02607, SYT2, PRKAG2, CHRNA7, GFRA2, CACNA2D3, ELAVL2, BTNL8, LINC01811, OTUD7A
PC_ 3
Positive: NRG1, AC024022.1, ETV1, NCKAP5, ROBO1, Ew-NG22, SAMD4A, FBN2, FBXL7, ESRRG
MTSS1, NHSL1, LRRC4C, OLFM2, ADCY9, MDGA2, AC007846.1, GPC6, DLGAP1, STK32B
SAMD5, ADAMTS2, LHFPL6, WNK2, ETV4, PAPPA, XYLT1, KCNQ3, FGFR1, PALM2-AKAP2
Negative: IQCJ-SCHIP1, LINC01331, PIK3C2B, IGF2BP3, LINGO2, AC022915.2, NETO1, AC087564.1, PCDH15, DCDC2C
ANO9, NIBAN1, TMEM132C, KCNMB2, ELL2, RGS6, PDZRN3, ANKRD33B, HOMER1, CNKSR3
EGR1, AL365295.1, IGSF11, ADGRG2, ITIH5, EPB41L2, AC025183.2, TSPAN5, ARHGAP6, AL591501.1
PC_ 4
Positive: AC007846.1, WNK2, FBN2, AC024022.1, ETV1, ETV4, LHX2, OLFM2, SAMD5, MDGA2
PHF21B, SHC3, VAT1L, STK32B, SHISA9, GCNT2, AC097512.1, NELL1, CALB2, AL033504.1
ADCY8, DLGAP1, NPTX2, PANTR1, SLC35F1, ESRRG, AC093607.1, LINC01393, CNR1, ENPP2
Negative: NAV3, BNC2, COL11A1, COL8A1, ITGBL1, FAP, NOX4, POSTN, COL12A1, FN1
COL3A1, TRDN, PDLIM3, RYR1, FRY, EPHA3, PRKG1, IGFBP7, PRICKLE1, COL5A1
DCN, NTM, ITGA11, ARHGAP24, COL5A2, LINC00578, TNS1, NEXN, AC108734.4, UACA
PC_ 5
Positive: GRB10, SNED1, CACNA1B, OTUD7A, CHI3L1, ENTHD1, FAM20C, ADAMTS17, CCSER1, PYGB
PRKAG2, SLC1A3, AC024901.1, COL1A1, RGS9, NFATC1, SYT2, TTN, DPF3, EPAS1
IGSF9B, SEMA4G, SYNDIG1, GALNT9, KCNAB2, AL596087.2, COL24A1, SLIT3, ELFN1, MAPT
Negative: IQGAP3, GTSE1, SPC25, RRM2, KIF18B, KNL1, CIT, TACC3, CDC25C, KIF15
NUSAP1, BUB1B, APOLD1, KIF4A, MKI67, HJURP, ARHGAP11B, ASPM, KIF11, CDCA2
TOP2A, NUF2, MELK, PRC1, CENPE, KIFC1, NCAPG, DLGAP5, ANLN, KIF14 ElbowPlot(Patient_Sarcoma_data_filtered)
##UMAP
Patient_Sarcoma_data_filtered <- RunUMAP(Patient_Sarcoma_data_filtered, dims = 1:15)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session12:17:25 UMAP embedding parameters a = 0.9922 b = 1.112
12:17:25 Read 44673 rows and found 15 numeric columns
12:17:25 Using Annoy for neighbor search, n_neighbors = 30
12:17:25 Building Annoy index with metric = cosine, n_trees = 50
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12:17:29 Writing NN index file to temp file /var/folders/05/_drvy3j57yb2pndzt041kp9c0n9q3g/T//Rtmp528C6e/file5375cc93345
12:17:29 Searching Annoy index using 1 thread, search_k = 3000
12:17:40 Annoy recall = 100%
12:17:41 Commencing smooth kNN distance calibration using 1 thread
12:17:43 Initializing from normalized Laplacian + noise
12:17:48 Commencing optimization for 200 epochs, with 1925260 positive edges
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12:18:17 Optimization finishedLab ID
Patient_Sarcoma_data_filtered@active.ident <- factor(Patient_Sarcoma_data_filtered$lab_id)Error in factor(Patient_Sarcoma_data_filtered$lab_id) :
object 'Patient_Sarcoma_data_filtered' not foundPatient_Sarcoma_data_filtered@active.ident <- factor(Patient_Sarcoma_data_filtered$lab_id)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'lab_id', label = T)
Genes
Find Neighbors
Patient_Sarcoma_data_filtered <- FindNeighbors(Patient_Sarcoma_data_filtered , dims=1:15) Computing nearest neighbor graph
Computing SNNPatient_Sarcoma_data_filtered <- FindClusters(Patient_Sarcoma_data_filtered,res = 1 )Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 44673
Number of edges: 1402609
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
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**************************************************|Maximum modularity in 10 random starts: 0.8728
Number of communities: 25
Elapsed time: 10 secondsLabeling Cells
Patient_Sarcoma_data_filtered$anno <- Patient_Sarcoma_data_filtered$seurat_clusters
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(16)] <- 'Skeletal Muscle'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(15,24,20)] <- 'Fibroblasts'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(19, 23)] <- 'Immune Cells'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(22)] <- 'Endothelial Cells'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(10)] <- 'CDS-1'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(1, 11,3, 8,12,13)] <- 'DSRCT-4'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(2,4,7,9,18,14,17)] <- 'DSRCT-2'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(21,0,5,6)] <- 'DSRCT-1'##View UMAPs
DimPlot(Patient_Sarcoma_data_filtered, label = T)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'lab_id', label = T)
classical_genes <- list(DSRCT = c( 'GAL', 'ST6GALNAC5',
'CACNA2D2' , 'KRT8', 'DES', 'NCAM1'),
CDS = c('ETV1', 'ETV4'),
'Endothelial\ncells' = c('VWF', 'PECAM1','CDH5'),
Fibroblasts = c('COL1A1', 'COL1A2', 'FN1'),
'Immune\ncells' = c('PTPRC', 'CD4', 'CD86'),
'Skeletal\nmuscle' = c('TTN', 'MYH3', 'NEB')
)
gene_dot_plot <-
DotPlot(Patient_Sarcoma_data_filtered,
features = classical_genes,
group.by = 'anno') + theme(axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 10
),
strip.text = element_text(size = 8)) + labs(x = '', y = '') + scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.gene_dot_plot
classical_genes <- list(DSRCT = c( 'GAL', 'ST6GALNAC5',
'CACNA2D2' , 'KRT8', 'DES', 'NCAM1'),
CDS = c('ETV1', 'ETV4'),
Fibroblasts = c('COL1A1', 'COL1A2', 'FN1'),
'Skeletal\nmuscle' = c('TTN', 'MYH3', 'NEB'),
'Immune\ncells' = c('PTPRC', 'CD4', 'CD86'),
'Endothelial\ncells' = c('VWF', 'PECAM1','CDH5')
)
gene_dot_plot <-
DotPlot(Patient_Sarcoma_data_filtered,
features = classical_genes,
group.by = 'anno') + theme(axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 10
),
strip.text = element_text(size = 7.5)) + labs(x = '', y = '') + scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.gene_dot_plot
###Figure
anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T) + ggtitle('') + theme(axis.text = element_blank())
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T) + ggtitle('') + theme(axis.text = element_blank())
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T) + ggtitle('') + theme(axis.text = element_blank())
tiff('../Figures/UO1_DSRCT_CDS_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T) + ggtitle('') + theme(axis.text = element_blank())
tiff('../Figures/UO1_DSRCT_CDS_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T) + ggtitle('') + theme(axis.text = element_blank())
tiff('../Figures/UO1_DSRCT_CDS_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T, repel = T) + ggtitle('') + theme(axis.text = element_blank())
tiff('../Figures/UO1_DSRCT_CDS_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)dev.off()null device
1 ##Gene signatures
gene_sigs <- read.delim("../gene_signatures.txt")
gene_sigs <- as.list(gene_sigs)
gene_sigs <- gene_sigs[c("EWS.FLI1" ,"EWS.WT1" ,"CIC.DUX4")]
gene_sigs <- lapply(gene_sigs, function(x) gsub(" ", "", x))
signature_names <- names(gene_sigs)
#generate a rename vector to easily rename each of the signatures
rename_vector <- setNames( paste0('Cluster', 1:length(signature_names)), paste0(signature_names, '_Signature'))
#use AddModuleScore to assess the signature expression in each library
Patient_Sarcoma_data_filtered <- AddModuleScore(Patient_Sarcoma_data_filtered, features = gene_sigs, name = 'Cluster', ctrl = 50)Warning: The following features are not present in the object: RP13-16H11.2, GPR64, RP11-1258F18.1, 1-Mar, AC022311.1, AC073135.3, FAM123A, FAM46A, FAM84B, KIAA1456, KIAA1462, LHFP, PRAC, RP11-18I14.10, RP11-252A24.7, RP11-521M14.1, RP11-6C14.1, RP13-16H11.1, , not searching for symbol synonymsWarning: The following features are not present in the object: , not searching for symbol synonymsWarning: The following features are not present in the object: , not searching for symbol synonyms#DSRCT_multiome@meta.data <- DSRCT_multiome@meta.data %>% dplyr::select(-c(names(rename_vector)))
Patient_Sarcoma_data_filtered@meta.data <- Patient_Sarcoma_data_filtered@meta.data %>% dplyr::rename(rename_vector)Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(rename_vector)
# Now:
data %>% select(all_of(rename_vector))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.VlnPlot(Patient_Sarcoma_data_filtered, group.by = 'anno',features = names(rename_vector))
VlnPlot(Patient_Sarcoma_data_filtered, group.by = 'anno',features = names(rename_vector)[2:3], combine = F)
[[1]]
[[2]]
signature_vln_plot_list
[[1]]
[[2]]
signature_vln_plot_list <- VlnPlot(Patient_Sarcoma_data_filtered, group.by = 'anno',features = names(rename_vector)[2:3], combine = F)
signature_vln_plot_list <- lapply(signature_vln_plot_list, function(x) x + theme(legend.position = 'none',
axis.title.x = element_blank()))
signature_vln_plot_list[[1]] <- signature_vln_plot_list[[1]] + ggtitle('EWS::WT1 Score')
signature_vln_plot_list[[2]] <- signature_vln_plot_list[[2]] + ggtitle('CIC::DUX4 Score')cic_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('CIC-', rownames(Patient_Sarcoma_data_filtered))]
cic_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = cic_ng, group.by = 'anno') + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10)) + labs(x='',y ='') + scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
cic_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = cic_ng, group.by = 'anno') + theme(axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 10
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c()
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.dsrct_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('DSRCT', rownames(Patient_Sarcoma_data_filtered))]
dsrct_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = dsrct_ng, group.by = 'anno') + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8)) + labs(x='',y ='') + scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.dsrct_ng_dp
dsrct_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('DSRCT', rownames(Patient_Sarcoma_data_filtered))]
dsrct_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = dsrct_ng, group.by = 'anno') + theme(axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 7.5
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c() + labs(x='',y ='') + scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.dsrct_ng_dp
###Figure
top <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1, labels = 'AUTO')
tiff('../Figures/UO1_DSRCT_CDS_signature.tiff', width = 8, height = 8, res = 300, units = 'in')
plot_grid(top, dsrct_ng_dp, cic_ng_dp, labels = c('', 'C', 'D'), nrow = 3)
top <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1, labels = 'AUTO')
tiff('../Figures/UO1_DSRCT_CDS_signature.tiff', width = 8, height = 10, res = 300, units = 'in')
plot_grid(top, dsrct_ng_dp, cic_ng_dp, labels = c('', 'C', 'D'), nrow = 3)dev.off()null device
1 top <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1, labels = 'AUTO')
tiff('../Figures/UO1_DSRCT_CDS_signature.tiff', width = 8, height = 9, res = 300, units = 'in')
plot_grid(top, dsrct_ng_dp, cic_ng_dp, labels = c('', 'C', 'D'), nrow = 3, rel_heights = c(1, 0.8,0.8))dev.off()null device
1 top <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1, labels = 'AUTO')
tiff('../Figures/UO1_DSRCT_CDS_signature.tiff', width = 8, height = 10, res = 300, units = 'in')
plot_grid(top, dsrct_ng_dp, cic_ng_dp, labels = c('', 'C', 'D'), nrow = 3, rel_heights = c(1, 0.8,0.8))dev.off()null device
1 DEGs
Patient_Clusters_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 1000)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~19s
|++ | 2 % ~17s
|++ | 3 % ~16s
|+++ | 4 % ~16s
|+++ | 5 % ~16s
|++++ | 6 % ~16s
|++++ | 7 % ~16s
|+++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~15s
|++++++ | 11% ~15s
|+++++++ | 12% ~15s
|+++++++ | 13% ~14s
|++++++++ | 14% ~14s
|++++++++ | 15% ~14s
|+++++++++ | 16% ~14s
|+++++++++ | 17% ~14s
|++++++++++ | 18% ~14s
|++++++++++ | 19% ~13s
|+++++++++++ | 20% ~13s
|+++++++++++ | 21% ~14s
|++++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 24% ~13s
|+++++++++++++ | 26% ~12s
|++++++++++++++ | 27% ~12s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~11s
|++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|+++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~10s
|+++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~09s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~15s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~14s
|++++ | 6 % ~14s
|++++ | 7 % ~13s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 10% ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 13% ~13s
|++++++++ | 14% ~13s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|++++++++++ | 20% ~12s
|+++++++++++ | 21% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~12s
|++++++++++++ | 24% ~12s
|+++++++++++++ | 25% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|++++++++++++++ | 28% ~11s
|+++++++++++++++ | 29% ~11s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|+++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|+++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~16s
|++ | 2 % ~16s
|++ | 3 % ~16s
|+++ | 4 % ~16s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 7 % ~15s
|+++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~15s
|++++++ | 11% ~14s
|+++++++ | 12% ~14s
|+++++++ | 13% ~14s
|++++++++ | 14% ~14s
|++++++++ | 15% ~14s
|+++++++++ | 16% ~14s
|+++++++++ | 17% ~14s
|++++++++++ | 18% ~14s
|++++++++++ | 19% ~14s
|+++++++++++ | 20% ~14s
|+++++++++++ | 21% ~13s
|++++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 24% ~13s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~12s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|+++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~14s
|++ | 3 % ~13s
|+++ | 4 % ~13s
|+++ | 5 % ~13s
|++++ | 6 % ~13s
|++++ | 7 % ~13s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 10% ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 13% ~12s
|++++++++ | 14% ~13s
|++++++++ | 15% ~13s
|+++++++++ | 16% ~13s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|++++++++++ | 20% ~12s
|+++++++++++ | 21% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|++++++++++++++ | 28% ~11s
|+++++++++++++++ | 29% ~11s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~10s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~09s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|+++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|+++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~19s
|++ | 2 % ~19s
|++ | 3 % ~18s
|+++ | 4 % ~18s
|+++ | 5 % ~18s
|++++ | 6 % ~17s
|++++ | 7 % ~17s
|+++++ | 8 % ~17s
|+++++ | 9 % ~17s
|++++++ | 10% ~17s
|++++++ | 11% ~16s
|+++++++ | 12% ~16s
|+++++++ | 13% ~16s
|++++++++ | 14% ~16s
|++++++++ | 15% ~15s
|+++++++++ | 16% ~15s
|+++++++++ | 17% ~15s
|++++++++++ | 18% ~15s
|++++++++++ | 19% ~15s
|+++++++++++ | 20% ~15s
|+++++++++++ | 21% ~14s
|++++++++++++ | 22% ~14s
|++++++++++++ | 23% ~14s
|+++++++++++++ | 24% ~14s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~13s
|+++++++++++++++ | 29% ~13s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|+++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 35% ~11s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|+++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 4 % ~11s
|+++ | 5 % ~11s
|++++ | 6 % ~11s
|++++ | 7 % ~11s
|+++++ | 8 % ~11s
|+++++ | 9 % ~11s
|++++++ | 10% ~11s
|++++++ | 11% ~11s
|+++++++ | 12% ~11s
|+++++++ | 13% ~11s
|++++++++ | 14% ~11s
|++++++++ | 15% ~11s
|+++++++++ | 16% ~11s
|+++++++++ | 17% ~11s
|++++++++++ | 18% ~11s
|++++++++++ | 19% ~10s
|+++++++++++ | 20% ~10s
|+++++++++++ | 21% ~10s
|++++++++++++ | 22% ~10s
|++++++++++++ | 23% ~10s
|+++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|++++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|+++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~09s
|++++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|+++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|++++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|+++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|+++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~13s
|+ | 2 % ~13s
|++ | 3 % ~13s
|++ | 4 % ~12s
|+++ | 5 % ~12s
|+++ | 6 % ~12s
|++++ | 7 % ~12s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|+++++ | 10% ~11s
|++++++ | 11% ~11s
|++++++ | 12% ~11s
|+++++++ | 13% ~11s
|+++++++ | 14% ~11s
|++++++++ | 15% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~10s
|++++++++++ | 20% ~10s
|+++++++++++ | 21% ~10s
|+++++++++++ | 22% ~10s
|++++++++++++ | 23% ~10s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|+++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~17s
|++ | 2 % ~16s
|++ | 3 % ~16s
|+++ | 4 % ~16s
|+++ | 5 % ~16s
|++++ | 6 % ~15s
|++++ | 7 % ~15s
|+++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~14s
|++++++ | 11% ~14s
|+++++++ | 12% ~14s
|+++++++ | 13% ~14s
|++++++++ | 14% ~14s
|++++++++ | 15% ~13s
|+++++++++ | 16% ~13s
|+++++++++ | 17% ~13s
|++++++++++ | 18% ~13s
|++++++++++ | 19% ~13s
|+++++++++++ | 20% ~14s
|+++++++++++ | 21% ~13s
|++++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 24% ~13s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|+++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~11s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|+++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~17s
|++ | 2 % ~17s
|++ | 3 % ~16s
|+++ | 4 % ~16s
|+++ | 5 % ~16s
|++++ | 6 % ~16s
|++++ | 7 % ~16s
|+++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~15s
|++++++ | 11% ~15s
|+++++++ | 12% ~15s
|+++++++ | 14% ~15s
|++++++++ | 15% ~14s
|++++++++ | 16% ~14s
|+++++++++ | 17% ~14s
|+++++++++ | 18% ~14s
|++++++++++ | 19% ~14s
|++++++++++ | 20% ~14s
|+++++++++++ | 21% ~14s
|+++++++++++ | 22% ~14s
|++++++++++++ | 23% ~13s
|++++++++++++ | 24% ~13s
|+++++++++++++ | 25% ~13s
|++++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|+++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|++++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|+++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|+++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|+++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~16s
|++ | 2 % ~15s
|++ | 3 % ~15s
|+++ | 4 % ~15s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 7 % ~15s
|+++++ | 8 % ~15s
|+++++ | 9 % ~14s
|++++++ | 10% ~14s
|++++++ | 11% ~14s
|+++++++ | 12% ~14s
|+++++++ | 13% ~14s
|++++++++ | 14% ~14s
|++++++++ | 15% ~14s
|+++++++++ | 16% ~13s
|+++++++++ | 17% ~13s
|++++++++++ | 18% ~13s
|++++++++++ | 19% ~13s
|+++++++++++ | 20% ~13s
|+++++++++++ | 21% ~13s
|++++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 24% ~13s
|+++++++++++++ | 25% ~12s
|++++++++++++++ | 26% ~12s
|++++++++++++++ | 27% ~12s
|+++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|++++++++++++++++ | 30% ~11s
|++++++++++++++++ | 31% ~11s
|+++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|+++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~10s
|++++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|+++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~10s
|++++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~09s
|+++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|++++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|+++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|+++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|+++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s Calculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~17s
|++ | 2 % ~16s
|++ | 3 % ~16s
|+++ | 4 % ~16s
|+++ | 5 % ~16s
|++++ | 6 % ~15s
|++++ | 7 % ~15s
|+++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~15s
|++++++ | 11% ~15s
|+++++++ | 12% ~15s
|+++++++ | 13% ~15s
|++++++++ | 14% ~15s
|++++++++ | 15% ~15s
|+++++++++ | 16% ~15s
|+++++++++ | 18% ~15s
|++++++++++ | 19% ~14s
|++++++++++ | 20% ~14s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++ | 21% ~14s
|+++++++++++ | 22% ~14s
|++++++++++++ | 23% ~14s
|++++++++++++ | 24% ~13s
|+++++++++++++ | 25% ~13s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~11s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|+++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|++++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~10s
|+++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~10s
|++++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|++++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|+++++++++++++++++++++++++ | 48% ~09s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|+++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|+++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~21s
|++ | 2 % ~25s
|++ | 3 % ~23s
|+++ | 4 % ~22s
|+++ | 5 % ~22s
|++++ | 6 % ~22s
|++++ | 8 % ~21s
|+++++ | 9 % ~20s
|+++++ | 10% ~19s
|++++++ | 11% ~18s
|++++++ | 12% ~19s
|+++++++ | 13% ~19s
|+++++++ | 14% ~18s
|++++++++ | 15% ~18s
|+++++++++ | 16% ~18s
|+++++++++ | 17% ~18s
|++++++++++ | 18% ~18s
|++++++++++ | 19% ~17s
|+++++++++++ | 20% ~17s
|+++++++++++ | 22% ~17s
|++++++++++++ | 23% ~16s
|++++++++++++ | 24% ~16s
|+++++++++++++ | 25% ~16s
|+++++++++++++ | 26% ~15s
|++++++++++++++ | 27% ~15s
|++++++++++++++ | 28% ~14s
|+++++++++++++++ | 29% ~14s
|++++++++++++++++ | 30% ~13s
|++++++++++++++++ | 31% ~13s
|+++++++++++++++++ | 32% ~13s
|+++++++++++++++++ | 33% ~12s
|++++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 35% ~12s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~09s
|++++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|+++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|+++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~12s
|++ | 2 % ~13s
|++ | 3 % ~13s
|+++ | 4 % ~13s
|+++ | 5 % ~13s
|++++ | 6 % ~12s
|++++ | 8 % ~12s
|+++++ | 9 % ~12s
|+++++ | 10% ~12s
|++++++ | 11% ~12s
|++++++ | 12% ~12s
|+++++++ | 13% ~12s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~11s
|+++++++++ | 17% ~11s
|++++++++++ | 18% ~11s
|++++++++++ | 19% ~11s
|+++++++++++ | 20% ~11s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|+++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 37% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|+++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s Calculating cluster 13
| | 0 % ~calculating
|+ | 1 % ~19s
|++ | 2 % ~18s
|++ | 3 % ~18s
|+++ | 4 % ~18s
|+++ | 5 % ~18s
|++++ | 6 % ~18s
|++++ | 7 % ~17s
|+++++ | 8 % ~17s
|+++++ | 9 % ~17s
|++++++ | 11% ~17s
|++++++ | 12% ~17s
|+++++++ | 13% ~17s
|+++++++ | 14% ~16s
|++++++++ | 15% ~16s
|++++++++ | 16% ~16s
|+++++++++ | 17% ~16s
|+++++++++ | 18% ~15s
|++++++++++ | 19% ~15s
|++++++++++ | 20% ~15s
|+++++++++++ | 21% ~15s
|++++++++++++ | 22% ~15s
|++++++++++++ | 23% ~14s
|+++++++++++++ | 24% ~14s
|+++++++++++++ | 25% ~14s
|++++++++++++++ | 26% ~14s
|++++++++++++++ | 27% ~13s
|+++++++++++++++ | 28% ~13s
|+++++++++++++++ | 29% ~13s
|++++++++++++++++ | 31% ~13s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|+++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 35% ~12s
|++++++++++++++++++ | 36% ~12s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~11s
|++++++++++++++++++++++ | 42% ~11s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|++++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~10s
|+++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|++++++++++++++++++++++++++ | 52% ~09s
|+++++++++++++++++++++++++++ | 53% ~09s
|+++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|+++++++++++++++++++++++++++++ | 58% ~08s
|++++++++++++++++++++++++++++++ | 59% ~07s
|++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~07s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|+++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s Calculating cluster 14
| | 0 % ~calculating
|+ | 1 % ~20s
|++ | 2 % ~20s
|++ | 3 % ~21s
|+++ | 4 % ~21s
|+++ | 5 % ~20s
|++++ | 6 % ~20s
|++++ | 7 % ~20s
|+++++ | 8 % ~20s
|+++++ | 9 % ~20s
|++++++ | 10% ~20s
|++++++ | 11% ~20s
|+++++++ | 12% ~20s
|+++++++ | 13% ~19s
|++++++++ | 14% ~19s
|++++++++ | 15% ~19s
|+++++++++ | 16% ~19s
|+++++++++ | 17% ~19s
|++++++++++ | 18% ~18s
|++++++++++ | 19% ~18s
|+++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|++++++++++++ | 22% ~17s
|++++++++++++ | 23% ~17s
|+++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~16s
|++++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~16s
|+++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~15s
|++++++++++++++++ | 30% ~15s
|++++++++++++++++ | 31% ~15s
|+++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|++++++++++++++++++ | 34% ~14s
|++++++++++++++++++ | 35% ~14s
|+++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|++++++++++++++++++++ | 38% ~13s
|++++++++++++++++++++ | 39% ~13s
|+++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|++++++++++++++++++++++ | 42% ~12s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|++++++++++++++++++++++++ | 46% ~11s
|++++++++++++++++++++++++ | 47% ~11s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|+++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21s Calculating cluster 15
| | 0 % ~calculating
|+ | 1 % ~20s
|++ | 2 % ~19s
|++ | 3 % ~18s
|+++ | 4 % ~18s
|+++ | 5 % ~18s
|++++ | 6 % ~18s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++ | 7 % ~18s
|+++++ | 8 % ~18s
|+++++ | 9 % ~17s
|++++++ | 10% ~17s
|++++++ | 11% ~17s
|+++++++ | 12% ~17s
|+++++++ | 13% ~17s
|++++++++ | 14% ~16s
|++++++++ | 15% ~16s
|+++++++++ | 16% ~17s
|+++++++++ | 17% ~16s
|++++++++++ | 18% ~16s
|++++++++++ | 19% ~16s
|+++++++++++ | 20% ~16s
|+++++++++++ | 21% ~15s
|++++++++++++ | 22% ~15s
|++++++++++++ | 23% ~15s
|+++++++++++++ | 24% ~15s
|+++++++++++++ | 25% ~14s
|++++++++++++++ | 26% ~14s
|++++++++++++++ | 27% ~14s
|+++++++++++++++ | 28% ~14s
|+++++++++++++++ | 29% ~13s
|++++++++++++++++ | 30% ~13s
|++++++++++++++++ | 31% ~13s
|+++++++++++++++++ | 32% ~13s
|+++++++++++++++++ | 33% ~13s
|++++++++++++++++++ | 34% ~13s
|++++++++++++++++++ | 35% ~12s
|+++++++++++++++++++ | 36% ~12s
|+++++++++++++++++++ | 37% ~12s
|++++++++++++++++++++ | 38% ~12s
|++++++++++++++++++++ | 39% ~12s
|+++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~11s
|++++++++++++++++++++++ | 42% ~11s
|++++++++++++++++++++++ | 43% ~11s
|+++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|++++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~10s
|+++++++++++++++++++++++++ | 48% ~10s
|+++++++++++++++++++++++++ | 49% ~10s
|++++++++++++++++++++++++++ | 51% ~09s
|++++++++++++++++++++++++++ | 52% ~09s
|+++++++++++++++++++++++++++ | 53% ~09s
|+++++++++++++++++++++++++++ | 54% ~09s
|++++++++++++++++++++++++++++ | 55% ~09s
|++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|+++++++++++++++++++++++++++++ | 58% ~08s
|++++++++++++++++++++++++++++++ | 59% ~08s
|++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|+++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~07s
|++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s Calculating cluster 16
| | 0 % ~calculating
|+ | 1 % ~15s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++ | 2 % ~15s
|++ | 3 % ~15s
|+++ | 4 % ~15s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 7 % ~14s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++ | 8 % ~14s
|+++++ | 9 % ~14s
|++++++ | 10% ~14s
|++++++ | 11% ~14s
|+++++++ | 12% ~14s
|+++++++ | 14% ~14s
|++++++++ | 15% ~14s
|++++++++ | 16% ~14s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++ | 17% ~14s
|+++++++++ | 18% ~14s
|++++++++++ | 19% ~14s
|++++++++++ | 20% ~14s
|+++++++++++ | 21% ~13s
|+++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|++++++++++++ | 24% ~13s
|+++++++++++++ | 25% ~12s
|++++++++++++++ | 26% ~12s
|++++++++++++++ | 27% ~12s
|+++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|++++++++++++++++ | 30% ~11s
|++++++++++++++++ | 31% ~11s
|+++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~09s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s Calculating cluster 17
| | 0 % ~calculating
|+ | 1 % ~21s
|++ | 2 % ~20s
|++ | 3 % ~20s
|+++ | 4 % ~20s
|+++ | 5 % ~20s
|++++ | 6 % ~20s
|++++ | 7 % ~20s
|+++++ | 8 % ~20s
|+++++ | 9 % ~19s
|++++++ | 10% ~19s
|++++++ | 11% ~19s
|+++++++ | 12% ~19s
|+++++++ | 13% ~19s
|++++++++ | 14% ~19s
|++++++++ | 15% ~19s
|+++++++++ | 16% ~19s
|+++++++++ | 17% ~18s
|++++++++++ | 18% ~18s
|++++++++++ | 19% ~18s
|+++++++++++ | 20% ~17s
|+++++++++++ | 21% ~17s
|++++++++++++ | 22% ~17s
|++++++++++++ | 23% ~17s
|+++++++++++++ | 24% ~16s
|+++++++++++++ | 25% ~16s
|++++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~16s
|+++++++++++++++ | 28% ~15s
|+++++++++++++++ | 29% ~15s
|++++++++++++++++ | 30% ~15s
|++++++++++++++++ | 31% ~15s
|+++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|++++++++++++++++++ | 34% ~14s
|++++++++++++++++++ | 35% ~14s
|+++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|++++++++++++++++++++ | 38% ~13s
|++++++++++++++++++++ | 39% ~13s
|+++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|++++++++++++++++++++++ | 42% ~12s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|++++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~11s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|+++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~08s
|++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|+++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=22s Calculating cluster 18
| | 0 % ~calculating
|+ | 1 % ~14s
|+ | 2 % ~15s
|++ | 3 % ~15s
|++ | 4 % ~14s
|+++ | 5 % ~14s
|+++ | 6 % ~13s
|++++ | 7 % ~13s
|++++ | 8 % ~13s
|+++++ | 9 % ~12s
|+++++ | 10% ~12s
|++++++ | 11% ~12s
|++++++ | 12% ~12s
|+++++++ | 13% ~12s
|+++++++ | 14% ~11s
|++++++++ | 15% ~11s
|++++++++ | 16% ~11s
|+++++++++ | 17% ~11s
|+++++++++ | 18% ~11s
|++++++++++ | 19% ~11s
|++++++++++ | 20% ~10s
|+++++++++++ | 21% ~10s
|+++++++++++ | 22% ~10s
|++++++++++++ | 23% ~10s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|+++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s Calculating cluster 19
| | 0 % ~calculating
|+ | 1 % ~17s
|+ | 2 % ~18s
|++ | 3 % ~17s
|++ | 4 % ~17s
|+++ | 5 % ~17s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++ | 6 % ~17s
|++++ | 7 % ~17s
|++++ | 8 % ~16s
|+++++ | 9 % ~16s
|+++++ | 10% ~16s
|++++++ | 11% ~16s
|++++++ | 12% ~16s
|+++++++ | 13% ~15s
|+++++++ | 14% ~15s
|++++++++ | 15% ~15s
|++++++++ | 16% ~15s
|+++++++++ | 17% ~15s
|+++++++++ | 18% ~15s
|++++++++++ | 19% ~14s
|++++++++++ | 20% ~14s
|+++++++++++ | 21% ~14s
|+++++++++++ | 22% ~14s
|++++++++++++ | 23% ~13s
|++++++++++++ | 24% ~13s
|+++++++++++++ | 25% ~13s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|+++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|+++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~07s
|+++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|+++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s Calculating cluster 20
| | 0 % ~calculating
|+ | 1 % ~13s
|+ | 2 % ~13s
|++ | 3 % ~13s
|++ | 4 % ~13s
|+++ | 5 % ~13s
|+++ | 6 % ~13s
|++++ | 7 % ~12s
|++++ | 8 % ~12s
|+++++ | 9 % ~12s
|+++++ | 10% ~12s
|++++++ | 11% ~12s
|++++++ | 12% ~12s
|+++++++ | 13% ~12s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|++++++++ | 16% ~11s
|+++++++++ | 17% ~11s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~11s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|+++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|+++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 21
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 2 % ~14s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~13s
|++++ | 6 % ~13s
|++++ | 7 % ~13s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 10% ~13s
|++++++ | 11% ~14s
|+++++++ | 12% ~13s
|+++++++ | 14% ~13s
|++++++++ | 15% ~13s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|++++++++++ | 20% ~12s
|+++++++++++ | 21% ~11s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|++++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~10s
|+++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|+++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|+++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~06s
|++++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|+++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 22
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 2 % ~14s
|++ | 3 % ~13s
|+++ | 4 % ~13s
|+++ | 5 % ~13s
|++++ | 6 % ~13s
|++++ | 7 % ~13s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 11% ~13s
|++++++ | 12% ~13s
|+++++++ | 13% ~12s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|++++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|++++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|+++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|+++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~08s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++ | 41% ~08s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|+++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|+++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 23
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~15s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++ | 3 % ~15s
|+++ | 4 % ~15s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 7 % ~14s
|+++++ | 8 % ~16s
|+++++ | 9 % ~16s
|++++++ | 10% ~16s
|++++++ | 11% ~15s
|+++++++ | 12% ~15s
|+++++++ | 14% ~15s
|++++++++ | 15% ~14s
|++++++++ | 16% ~14s
|+++++++++ | 17% ~14s
|+++++++++ | 18% ~14s
|++++++++++ | 19% ~14s
|++++++++++ | 20% ~13s
|+++++++++++ | 21% ~13s
|+++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|++++++++++++ | 24% ~13s
|+++++++++++++ | 25% ~13s
|++++++++++++++ | 26% ~12s
|++++++++++++++ | 27% ~12s
|+++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|++++++++++++++++ | 30% ~11s
|++++++++++++++++ | 31% ~11s
|+++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~09s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~08s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~05s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s Calculating cluster 24
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 2 % ~13s
|++ | 3 % ~13s
|+++ | 4 % ~13s
|+++ | 5 % ~13s
|++++ | 6 % ~13s
|++++ | 7 % ~13s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 10% ~12s
|++++++ | 11% ~12s
|+++++++ | 12% ~12s
|+++++++ | 13% ~12s
|++++++++ | 14% ~12s
|++++++++ | 15% ~11s
|+++++++++ | 16% ~11s
|+++++++++ | 18% ~11s
|++++++++++ | 19% ~11s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|+++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~08s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|+++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|+++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s saveRDS(Patient_Clusters_DEGs, '../RDS/2023-11-8 Patient_Clusters_DEGs.rds')top_Patient_Clusters_DEGs <- Patient_Clusters_DEGs %>% group_by(cluster) %>% top_n(10, avg_log2FC)```r
cic_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('CIC-', rownames(Patient_Sarcoma_data_filtered))]
DotPlot(Patient_Sarcoma_data_filtered, features = cic_ng)
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
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" 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" />
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#Save Data
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc2F2ZVJEUygnLi4vUkRTLzIwMjMtMTEtMTNfUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWQucmRzJylcblxuYGBgIn0= -->
```r
saveRDS('../RDS/2023-11-13_Patient_Sarcoma_data_filtered.rds')
Error in saveRDS("../RDS/2023-11-13_Patient_Sarcoma_data_filtered.rds") :
'file' must be non-empty string#Split the data
```r
Patient_Sarcoma_data_filtered_list <- SplitObject(Patient_Sarcoma_data_filtered, split.by = 'lab_id')
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCA8LSBsYXBwbHkoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCwgZnVuY3Rpb24oeCl7XG4gIHggJT4lIFxuICBOb3JtYWxpemVEYXRhKCkgJT4lXG4gIEZpbmRWYXJpYWJsZUZlYXR1cmVzKCkgJT4lXG4gIFNjYWxlRGF0YSgpICU+JVxuICBSdW5QQ0EoKVxufSlcbmBgYFxuYGBgIn0= -->
```r
```r
Patient_Sarcoma_data_filtered_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x){
x %>%
NormalizeData() %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA()
})
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGFwcGx5KFBhdGllbnRfU2FyY29tYV9kYXRhX2ZpbHRlcmVkX2xpc3QsRWxib3dQbG90KVxuYGBgXG5gYGAifQ== -->
```r
```r
lapply(Patient_Sarcoma_data_filtered_list,ElbowPlot)
<!-- rnb-source-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCA8LSBsYXBwbHkoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCwgZnVuY3Rpb24oeCl7XG4gIFJ1blVNQVAoeCwgZGltcyA9IDE6MTUpXG59KVxuYGBgXG5gYGAifQ== -->
```r
```r
Patient_Sarcoma_data_filtered_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x){
RunUMAP(x, dims = 1:15)
})
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##Clusters
<!-- rnb-text-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCA8LSBsYXBwbHkoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCwgZnVuY3Rpb24oeCl7XG4gIHggPC0gIEZpbmROZWlnaGJvcnMoeCAsIGRpbXM9MToxNSkgXG4gIHggPC0gRmluZENsdXN0ZXJzKHggKVxufSlcbmBgYFxuYGBgIn0= -->
```r
```r
Patient_Sarcoma_data_filtered_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x){
x <- FindNeighbors(x , dims=1:15)
x <- FindClusters(x )
})
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
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<!-- rnb-text-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGFwcGx5KFBhdGllbnRfU2FyY29tYV9kYXRhX2ZpbHRlcmVkX2xpc3QsIGZ1bmN0aW9uKHgpXG4gIERpbVBsb3QoeCwgbGFiZWw9IFQpICsgTm9MZWdlbmQoKSlcbmBgYFxuYGBgIn0= -->
```r
```r
lapply(Patient_Sarcoma_data_filtered_list, function(x)
DimPlot(x, label= T) + NoLegend())
<!-- rnb-source-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGFwcGx5KFBhdGllbnRfU2FyY29tYV9kYXRhX2ZpbHRlcmVkX2xpc3QsIGZ1bmN0aW9uKHgpXG4gIEZlYXR1cmVQbG90KHgsICdXVDEnKSlcbmBgYFxuYGBgIn0= -->
```r
```r
lapply(Patient_Sarcoma_data_filtered_list, function(x)
FeaturePlot(x, 'WT1'))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##DEGs
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuUGF0aWVudF9DbHVzdGVyc19ERUdfbGlzdCA8LSBsYXBwbHkoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCwgZnVuY3Rpb24oeCkgRmluZEFsbE1hcmtlcnMoeCwgb25seS5wb3MgPSBULCB0ZXN0LnVzZSA9ICdMUicsIHJldHVybi50aHJlc2ggPSAwLjA1LCBtYXguY2VsbHMucGVyLmlkZW50ID0gMjAwKSlcbmBgYFxuYGBgIn0= -->
```r
```r
Patient_Clusters_DEG_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x) FindAllMarkers(x, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 200))
<!-- rnb-source-end -->
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```r
```r
saveRDS(Patient_Clusters_DEG_list, '../RDS/2023-05-01-Patient_Clusters_DEG_list.rds')
<!-- rnb-source-end -->
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```r
```r
Patient_Clusters_DEG_list_top <- lapply(Patient_Clusters_DEG_list, function(x)
x %>% group_by(cluster) %>% top_n(10, avg_log2FC))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##DotPlots
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2VuZXMgPC0gcm93bmFtZXMoUERYX25lb2dlbmVzX2ZpbHRlcmVkKVtncmVwKCdEU1JDVC1ORycsIHJvd25hbWVzKFBEWF9uZW9nZW5lc19maWx0ZXJlZCkpXVxubGFwcGx5KFBhdGllbnRfU2FyY29tYV9kYXRhX2ZpbHRlcmVkX2xpc3QsZnVuY3Rpb24oeCkgRG90UGxvdCh4LCBmZWF0dXJlcyA9IGdlbmVzKSArIHRoZW1lKGF4aXMudGV4dC54ID0gZWxlbWVudF90ZXh0KGFuZ2xlID00NSwgaGp1c3Q9MSkpKVxuYGBgXG5gYGAifQ== -->
```r
```r
genes <- rownames(PDX_neogenes_filtered)[grep('DSRCT-NG', rownames(PDX_neogenes_filtered))]
lapply(Patient_Sarcoma_data_filtered_list,function(x) DotPlot(x, features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1)))
<!-- rnb-source-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2VuZXMgPC0gYygnRFNSQ1QtTkcyJywgJ1BUUFJDJywgJ0NPTDFBMScsICdBQ1RBMicsICdQRUNBTTEnKVxuXG5sYXBwbHkoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCxmdW5jdGlvbih4KSBEb3RQbG90KHgsIGZlYXR1cmVzID0gZ2VuZXMpICsgdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoYW5nbGUgPTQ1LCBoanVzdD0xKSkpXG5gYGBcbmBgYCJ9 -->
```r
```r
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1')
lapply(Patient_Sarcoma_data_filtered_list,function(x) DotPlot(x, features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1)))
<!-- rnb-source-end -->
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```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-2` <- FindSubCluster(Patient_Sarcoma_data_filtered_list$`DSRCT-2`,
c('11'), 'RNA_snn')
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###DSRCT-2
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2VuZXMgPC0gYygnRFNSQ1QtTkcyJywgJ1BUUFJDJywgJ0NPTDFBMScsICdBQ1RBMicsICdQRUNBTTEnLCAnVldGJywgJ0lMN1InKVxuXG5Eb3RQbG90KFBhdGllbnRfU2FyY29tYV9kYXRhX2ZpbHRlcmVkX2xpc3QkYERTUkNULTJgLCBncm91cC5ieSA9ICdzdWIuY2x1c3RlcicsIGZlYXR1cmVzID0gZ2VuZXMpICsgdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoYW5nbGUgPTQ1LCBoanVzdD0xKSlcblxuRGltUGxvdChQYXRpZW50X1NhcmNvbWFfZGF0YV9maWx0ZXJlZF9saXN0JGBEU1JDVC0yYCwgZ3JvdXAuYnkgPSAnc3ViLmNsdXN0ZXInLCBsYWJlbCA9IFQpXG5gYGBcbmBgYCJ9 -->
```r
```r
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1', 'VWF', 'IL7R')
DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, group.by = 'sub.cluster', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, group.by = 'sub.cluster', label = T)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
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<!-- rnb-text-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-2`$Anno <- Patient_Sarcoma_data_filtered_list$`DSRCT-2`$seurat_clusters
levels(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$Anno),'_',
c(rep('DSRCT',10),
'Fibroblasts',
'Immune Cells'))
Patient_Sarcoma_data_filtered_list$`DSRCT-2`$cell_type <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$seurat_clusters)
levels(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$cell_type) <-c(rep('DSRCT',10),
'Fibroblasts',
'Immune Cells')
DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, group.by = 'cell_type', label = T)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2VuZXMgPC0gcm93bmFtZXMoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCRgRFNSQ1QtMmApW2dyZXAoJ0RTUkNULU5HJywgcm93bmFtZXMoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCRgRFNSQ1QtMmApKV1cbkRvdFBsb3QoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCRgRFNSQ1QtMmAsIGZlYXR1cmVzID0gZ2VuZXMsIGdyb3VwLmJ5ID0gJ0Fubm8nKSArIHRoZW1lKGF4aXMudGV4dC54ID0gZWxlbWVudF90ZXh0KGFuZ2xlID00NSwgaGp1c3Q9MSkpXG5gYGBcbmBgYCJ9 -->
```r
```r
genes <- rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-2`)[grep('DSRCT-NG', rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-2`))]
DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, features = genes, group.by = 'Anno') + theme(axis.text.x = element_text(angle =45, hjust=1))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###DSRCT-4
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCRgRFNSQ1QtNGAgPC0gRmluZFN1YkNsdXN0ZXIoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdCRgRFNSQ1QtNGAsIFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgYygnMTEnKSwgJ1JOQV9zbm4nKVxuYGBgXG5gYGAifQ== -->
```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-4` <- FindSubCluster(Patient_Sarcoma_data_filtered_list$`DSRCT-4`,
c('11'), 'RNA_snn')
<!-- rnb-source-end -->
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```r
```r
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1', 'VWF', 'FAP', 'WT1')
DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'sub.cluster', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2VuZXMgPC0gYygnRFNSQ1QtTkcyJywgJ0RTUkNULU5HNicsICdNS0k2NycsICdaRUIxJywgJ1dUMScsJ1BER0ZSQScsIFxuICAgICAgICAgICAnQ09MMUExJywgJ0FDVEEyJywgJ1BFQ0FNMScsICAnSUw3UicsICAgJ0NEMTYzJywgJ0NENjgnLCAnVldGJylcblxuRG90UGxvdChQYXRpZW50X1NhcmNvbWFfZGF0YV9maWx0ZXJlZF9saXN0JGBEU1JDVC00YCwgZ3JvdXAuYnkgPSAnQW5ubycsIGZlYXR1cmVzID0gZ2VuZXMpICsgdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoYW5nbGUgPTQ1LCBoanVzdD0xKSlcbmBgYFxuYGBgIn0= -->
```r
```r
genes <- c('DSRCT-NG2', 'DSRCT-NG6', 'MKI67', 'ZEB1', 'WT1','PDGFRA',
'COL1A1', 'ACTA2', 'PECAM1', 'IL7R', 'CD163', 'CD68', 'VWF')
DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'Anno', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
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```r
```r
genes <- rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-4`)[grep('DSRCT-NG', rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-4`))]
DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'Anno', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
<!-- rnb-source-end -->
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```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno <- Patient_Sarcoma_data_filtered_list$`DSRCT-4`$seurat_clusters
levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno),'_',
c(rep('DSRCT',11),
'Fibroblasts', #Mesothelial Cells
'Immune Cells', #Myeloid Cells
'Immune Cells')) #T-Cells
Patient_Sarcoma_data_filtered_list$`DSRCT-4`$cell_type <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$seurat_clusters)
levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$cell_type) <- c(rep('DSRCT',11),
'Fibroblasts', #Mesothelial Cells
'Immune Cells', #Myeloid Cells
'Immune Cells') #T-Cells
DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'cell_type', label = T)
<!-- rnb-source-end -->
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###DSRCT-1
<!-- rnb-text-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-1`@active.ident <-factor( Patient_Sarcoma_data_filtered_list$`DSRCT-1`$seurat_clusters)
Patient_Sarcoma_data_filtered_list$`DSRCT-1` <- FindSubCluster(Patient_Sarcoma_data_filtered_list$`DSRCT-1`,
10, 'RNA_snn', res = 1)
DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'sub.cluster', label = T )
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```r
```r
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1', 'VWF', 'IL7R', 'CD68')
DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'sub.cluster', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
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```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-1`@active.ident <-factor( Patient_Sarcoma_data_filtered_list$`DSRCT-1`$sub.cluster)
Patient_Sarcoma_data_filtered_list$`DSRCT-1`$Anno <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$sub.cluster)
levels(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$Anno),'_',
c(rep('DSRCT',2),
'Fibroblasts',
'Neuronal',
'Immune Cells',
rep('DSRCT',8)))
Patient_Sarcoma_data_filtered_list$`DSRCT-1`$cell_type <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$sub.cluster)
levels(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$cell_type) <- c(rep('DSRCT',2),
'Fibroblasts',
'Immune Cells',
'Neuronal Cells',
rep('DSRCT',8))
DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'cell_type', label = T)
<!-- rnb-source-end -->
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```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-1`@active.ident <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$cell_type)
DSRCT_1_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'cell_type',
only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 200)
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```r
```r
FeaturePlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, features = 'NEUROD1')
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```r
```r
Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno <- Patient_Sarcoma_data_filtered_list$`DSRCT-1`$seurat_clusters
levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno),'_',
c(rep('DSRCT',11),
'Mesothelial Cells',
'Myeloid Cells',
'T-Cells'))
DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'Anno', label = T)
<!-- rnb-source-end -->
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```r
```r
lapply(Patient_Sarcoma_data_filtered_list[1:3], function(x) DimPlot(x, group.by = 'Anno', label = T))
<!-- rnb-source-end -->
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#Extracting DSRCT data from PDX and patients
Patient data will be exclusively from nuclei data
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuRFNSQ1Rfb25seV9jZWxscyA8LSBsYXBwbHkoUGF0aWVudF9TYXJjb21hX2RhdGFfZmlsdGVyZWRfbGlzdFsxOjNdLCBmdW5jdGlvbih4KSB4Wyx4JGNlbGxfdHlwZSA9PSAnRFNSQ1QnXSApXG5EU1JDVF9vbmx5X2NlbGxzX2RhdGFzZXQgPC0gbWVyZ2UoRFNSQ1Rfb25seV9jZWxsc1tbMV1dLCBEU1JDVF9vbmx5X2NlbGxzWzI6M10pXG5EU1JDVF9vbmx5X2NlbGxzX2RhdGFzZXQkc291cmNlID0gJ1BhdGllbnQnXG5gYGBcbmBgYCJ9 -->
```r
```r
DSRCT_only_cells <- lapply(Patient_Sarcoma_data_filtered_list[1:3], function(x) x[,x$cell_type == 'DSRCT'] )
DSRCT_only_cells_dataset <- merge(DSRCT_only_cells[[1]], DSRCT_only_cells[2:3])
DSRCT_only_cells_dataset$source = 'Patient'
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```r
```r
DSRCT_PDX_cells_dataset <- PDX_neogenes_filtered[,PDX_neogenes_filtered$lab_id %in% c('DSRCT-1', 'DSRCT-2', 'DSRCT-4')]
DSRCT_PDX_cells_dataset$source = 'PDX'
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```r
```r
DSRCT_dataset <- merge(DSRCT_only_cells_dataset, DSRCT_PDX_cells_dataset)
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```r
```r
DSRCT_dataset$library <- paste0(DSRCT_dataset$lab_id, '_', DSRCT_dataset$source)
DSRCT_dataset@active.ident <- factor(DSRCT_dataset$library)
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```r
```r
genes <- rownames(DSRCT_dataset)[grep('DSRCT-NG', rownames(DSRCT_dataset))]
DotPlot(DSRCT_dataset, features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
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```r
```r
DSRCT_dataset_avg <- AverageExpression(DSRCT_dataset)
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```r
```r
genes <- rownames(DSRCT_dataset)[grep('-NG', rownames(DSRCT_dataset))]
mat <- DSRCT_dataset_avg$RNA[genes,]
pheatmap::pheatmap(cor(mat))
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```r
```r
require(ggrepel)
pca <- prcomp(t(mat))
pca_df <- data.frame(PC_1 = pca$x[,1], PC_2 = pca$x[,2], labels = rownames(pca$x))
ggplot(pca_df, aes(x = PC_1, y = PC_2, label = labels)) +
geom_text_repel() +
labs(x= paste0('PC_1 ', '(',round(pca$sdev[1],2), '%)'),
y= paste0('PC_2 ', '(',round(pca$sdev[2],2), '%)'))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#Processing (excluding CDS)
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuUGF0aWVudF9TYXJjb21hX2RhdGEgPC0gc3Vic2V0KFBhdGllbnRfU2FyY29tYV9kYXRhLCAnbGFiX2lkJyAhPSAnQ0RTLTEnKVxuXG5gYGAifQ== -->
```r
Patient_Sarcoma_data <- subset(Patient_Sarcoma_data, 'lab_id' != 'CDS-1')
Error in FetchData.Seurat(object = object, vars = unique(x = expr.char[vars.use]), :
None of the requested variables were found: ##filter out low quality cells
Patient_Sarcoma_data_filtered <- subset(Patient_Sarcoma_data,
subset =
percent.mt < 10 & nCount_RNA > 1000 & nFeature_RNA > 500)
Patient_Sarcoma_dataAn object of class Seurat
37002 features across 40375 samples within 1 assay
Active assay: RNA (37002 features, 0 variable features)Patient_Sarcoma_data_filteredAn object of class Seurat
37002 features across 40182 samples within 1 assay
Active assay: RNA (37002 features, 0 variable features)##normalize
Patient_Sarcoma_data_filtered <- Patient_Sarcoma_data_filtered %>%
NormalizeData() %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA()Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
|
| | 0%
|
|====================================================== | 50%
|
|=============================================================================================================| 100%
PC_ 1
Positive: ITIH5, NKAIN3, CACNA2D1, COL6A3, CHRM3, TTC28, NLGN1, CELF4, CASC15, ZBTB7C
PVT1, ADAMTSL1, ST6GAL1, FAM20C, PLEKHG4B, THSD7B, AC068587.4, COL4A2, ZNF385B, SMARCD3
NTRK3, DPYSL3, ANO4, DPF3, PCBP3, TENM3, MATN2, GRID1, AC009518.1, KCNAB2
Negative: ANK3, PTPRM, STXBP6, AC104041.1, ERBB4, MAP7, GRHL2, AGBL1, CSF2RA, PLK5
THSD7A, AR, ALCAM, FMN2, AL033523.1, DSRCT-NG18, ARHGAP8, GALNT17, MLPH, AL117329.1
FIGN, AL583808.1, SMOC2, TMTC2, COLEC12, AC013652.1, RIMS1, BMPR1B, PRKAR2B, INSR
PC_ 2
Positive: CCSER1, SDK1, GRB10, LINC01876, COL24A1, PDE3A, CASC15, MIR34AHG, MATN2, PRKAG2
LINC01811, KCNMA1, PLEKHG4B, PGBD5, DPF3, ADAMTSL1, DISP3, PVT1, ZNF385B, PLCL1
SORCS2, AC011246.1, ANO4, FAM20C, PCBP3, AC078845.1, ATP2B4, LINC02607, SMARCD3, DIAPH2
Negative: PDZRN3, IGF2BP3, PIK3C2B, TMEM132C, NIBAN1, NETO1, AC087564.1, EPB41L2, AC022915.2, ELL2
PCDH15, DCDC2C, ANKRD33B, PRKG1, LINC01331, ADARB2, IQCJ-SCHIP1, EGR1, CNKSR3, ANO9
LRRK2, RGS6, KCNMB2, LINGO2, AL365295.1, ADGRG2, TSPAN5, AC025183.2, ADGRL3, TPRG1
PC_ 3
Positive: GRB10, CACNA1B, OTUD7A, SNED1, ADAMTS17, KCNMA1, FAM20C, CHI3L1, ENTHD1, CCSER1
PRKAG2, PYGB, SORCS2, CHST15, AC024901.1, ELFN1, TAFA5, RGS9, SYT2, SPON2
NFATC1, SEMA4G, SLC1A3, MIR34AHG, NTRK3, GALNT9, KCNAB2, EPAS1, PDE10A, HIST1H2AC
Negative: IQGAP3, GTSE1, SPC25, TACC3, RRM2, KNL1, CIT, KIF18B, KIF15, CDC25C
APOLD1, NUSAP1, BUB1B, KIF4A, RGS3, HJURP, MKI67, MELK, ARHGAP11B, ASPM
KIF11, PRC1, CDCA2, TOP2A, NUF2, KIFC1, CENPE, DLGAP5, NCAPG, ANLN
PC_ 4
Positive: NTRK3, ADAMTSL2, PCBP3, CPLX1, DSRCT-NG31, SYT2, DISP3, DSRCT-NG36, ADAMTS17, TG
DSRCT-NG20, DSRCT-NG11, GRTP1, KCNIP1, DSRCT-NG13, DIP2C, LRFN2, CHRNA7, OVOL2, ADARB1
PWRN1, GFRA2, NAE1, PGBD5, PHEX, TGM7, ERC2, AC009518.1, AC078845.1, MIR34AHG
Negative: BNC2, XYLT1, RAB31, NAV3, COL5A2, COL8A1, IRAK3, COL5A1, LDB2, CRISPLD2
COL3A1, PCSK5, SAMD4A, COL16A1, COL1A2, FAP, CPNE8, IGFBP7, SLC9A9, IL16
FBXL7, AC079298.3, IFI16, MAN1A1, CPQ, SERPINE1, SEC24D, DLC1, PLD1, BCAT1
PC_ 5
Positive: DOCK2, PIK3R5, AOAH, ARHGAP15, CD163, FYB1, SLCO2B1, LCP2, RGS1, INPP5D
HCLS1, CPM, MSR1, SAMSN1, PDE3B, SRGN, APBB1IP, MS4A7, TLR2, CD53
LYN, TNFRSF1B, LAPTM5, PRKCH, SLC8A1, MS4A6A, CIITA, CD74, PTPRC, RCSD1
Negative: COL5A2, NAV3, COL8A1, COL5A1, COL3A1, COL16A1, COL1A2, CRISPLD2, FAP, LDB2
FBXL7, AC079298.3, IGFBP7, CCN4, DLC1, PRICKLE1, NTM, CCN1, DCN, COL11A1
GPC6, FN1, LSAMP, AL445426.1, COL12A1, PDE1A, PDLIM3, HMCN1, TIMP3, PCSK5 ElbowPlot(Patient_Sarcoma_data_filtered)
##UMAP
Patient_Sarcoma_data_filtered <- RunUMAP(Patient_Sarcoma_data_filtered, dims = 1:50)09:58:16 UMAP embedding parameters a = 0.9922 b = 1.112
09:58:16 Read 40182 rows and found 50 numeric columns
09:58:16 Using Annoy for neighbor search, n_neighbors = 30
09:58:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:58:19 Writing NN index file to temp file /var/folders/05/_drvy3j57yb2pndzt041kp9c0n9q3g/T//RtmprvESKL/file417226be1138
09:58:19 Searching Annoy index using 1 thread, search_k = 3000
09:58:29 Annoy recall = 100%
09:58:32 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
09:58:37 Initializing from normalized Laplacian + noise (using irlba)
09:58:39 Commencing optimization for 200 epochs, with 1861948 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:59:07 Optimization finishedLab ID
Patient_Sarcoma_data_filtered@active.ident <- factor(Patient_Sarcoma_data_filtered$lab_id)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'lab_id', label = T)
Find Neighbors
DEGs
Patient_Clusters_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 500)Patient_Sarcoma_data_filtered_subset <- subset(Patient_Sarcoma_data_filtered, Patient_Sarcoma_data_filtered$seurat_clusters %in% c('9','10'))
Error in FetchData.Seurat(object = object, vars = unique(x = expr.char[vars.use]), :
None of the requested variables were found: Patient_Sarcoma_data_filtered_subset <- FindClusters(Patient_Sarcoma_data_filtered_subset)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 447
Number of edges: 10285
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8460
Number of communities: 9
Elapsed time: 0 secondsPatient_Sarcoma_data_filtered_subset_Clusters_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered_subset, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 500)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~06s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|+++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~07s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 14% ~07s
|++++++++ | 15% ~07s
|++++++++ | 16% ~07s
|+++++++++ | 17% ~07s
|+++++++++ | 18% ~07s
|++++++++++ | 19% ~07s
|++++++++++ | 20% ~07s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|+++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|+++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~09s
|++ | 2 % ~09s
|++ | 3 % ~30s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++ | 4 % ~25s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++ | 5 % ~22s
|++++ | 6 % ~20s
|++++ | 7 % ~18s
|+++++ | 8 % ~17s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++ | 9 % ~16s
|++++++ | 10% ~15s
|++++++ | 11% ~14s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++ | 12% ~14s
|+++++++ | 14% ~13s
|++++++++ | 15% ~13s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|+++++++++ | 18% ~11s
|++++++++++ | 19% ~11s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++ | 20% ~11s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++ | 21% ~11s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++ | 22% ~10s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++ | 23% ~10s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~09s
|++++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|+++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|++++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|+++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~06s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++ | 45% ~06s
|+++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~05s
|+++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++ | 58% ~05s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++ | 59% ~05s
|+++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~04s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++ | 69% ~04s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~07s
|+ | 2 % ~07s
|++ | 3 % ~07s
|++ | 4 % ~07s
|+++ | 5 % ~07s
|+++ | 6 % ~06s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++ | 7 % ~06s
|++++ | 8 % ~06s
|+++++ | 9 % ~06s
|+++++ | 10% ~06s
|++++++ | 11% ~06s
|++++++ | 12% ~06s
|+++++++ | 13% ~06s
|+++++++ | 14% ~06s
|++++++++ | 15% ~06s
|++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|+++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|++++++++++ | 20% ~06s
|+++++++++++ | 21% ~05s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~06s
|++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
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|++++++++++++++++++++ | 39% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~04s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~09s
|++ | 3 % ~08s
|+++ | 4 % ~08s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~06s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~05s
|++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|+++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++ | 51% ~04s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~11s
|++ | 3 % ~10s
|+++ | 4 % ~10s
|+++ | 5 % ~10s
|++++ | 6 % ~10s
|++++ | 7 % ~10s
|+++++ | 8 % ~10s
|+++++ | 9 % ~10s
|++++++ | 10% ~10s
|++++++ | 11% ~09s
|+++++++ | 12% ~09s
|+++++++ | 13% ~09s
|++++++++ | 14% ~09s
|++++++++ | 15% ~09s
|+++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|++++++++++ | 18% ~09s
|++++++++++ | 19% ~09s
|+++++++++++ | 20% ~09s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|+++++++++++++ | 24% ~08s
|+++++++++++++ | 25% ~08s
|++++++++++++++ | 26% ~08s
|++++++++++++++ | 27% ~08s
|+++++++++++++++ | 28% ~08s
|+++++++++++++++ | 29% ~08s
|++++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|+++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|++++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|+++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|++++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~11s
|+ | 2 % ~11s
|++ | 3 % ~11s
|++ | 4 % ~11s
|+++ | 5 % ~11s
|+++ | 6 % ~11s
|++++ | 7 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|+++++ | 10% ~11s
|++++++ | 11% ~10s
|++++++ | 12% ~10s
|+++++++ | 13% ~10s
|+++++++ | 14% ~10s
|++++++++ | 15% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~09s
|++++++++++ | 20% ~09s
|+++++++++++ | 21% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|+++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~08s
|+++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~11s
|+ | 2 % ~11s
|++ | 3 % ~11s
|++ | 4 % ~10s
|+++ | 5 % ~10s
|+++ | 6 % ~10s
|++++ | 7 % ~10s
|++++ | 8 % ~10s
|+++++ | 9 % ~10s
|+++++ | 10% ~10s
|++++++ | 11% ~10s
|++++++ | 12% ~10s
|+++++++ | 13% ~10s
|+++++++ | 14% ~09s
|++++++++ | 15% ~09s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~09s
|++++++++++ | 20% ~09s
|+++++++++++ | 21% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|+++++++++++++ | 26% ~09s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++ | 27% ~08s
|++++++++++++++ | 28% ~08s
|+++++++++++++++ | 29% ~08s
|+++++++++++++++ | 30% ~08s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++ | 31% ~08s
|++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 37% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~08s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~09s
|+ | 2 % ~08s
|++ | 3 % ~08s
|++ | 4 % ~08s
|+++ | 5 % ~08s
|+++ | 6 % ~07s
|++++ | 7 % ~07s
|++++ | 8 % ~07s
|+++++ | 9 % ~07s
|+++++ | 10% ~07s
|++++++ | 11% ~07s
|++++++ | 12% ~07s
|+++++++ | 13% ~07s
|+++++++ | 14% ~07s
|++++++++ | 15% ~07s
|++++++++ | 16% ~07s
|+++++++++ | 17% ~07s
|+++++++++ | 18% ~07s
|++++++++++ | 19% ~07s
|++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|+++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|+++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s DimPlot(Patient_Sarcoma_data_filtered_subset, label = T)
Patient_Sarcoma_data_filtered_subset$anno <- Patient_Sarcoma_data_filtered_subset$seurat_clusters
levels(Patient_Sarcoma_data_filtered_subset$anno) <- c('Fibroblasts', 'Fibroblasts', 'T-cells',
'Endothelial cells', 'Myeloid cells', 'Myeloid cells',
'Fibroblasts', 'DSRCT-2', 'Myeloid cells')Patient_Sarcoma_data_filtered$anno <- Patient_Sarcoma_data_filtered$seurat_clusters
levels(Patient_Sarcoma_data_filtered$anno) <- c('DSRCT-1', 'DSRCT-2', 'DSRCT-4', 'DSRCT-4' ,'DSRCT-2', 'DSRCT-4', 'DSRCT-4', 'DSRCT-4', 'DSRCT-2',
'9', '10', 'DSRCT-1', 'DSRCT-4', 'DSRCT-2')
Patient_Sarcoma_data_filtered$anno <- as.character(Patient_Sarcoma_data_filtered$anno)
Patient_Sarcoma_data_filtered@meta.data[colnames(Patient_Sarcoma_data_filtered_subset),'anno'] <- as.character(Patient_Sarcoma_data_filtered_subset$anno)
Patient_Sarcoma_data_filtered$anno <- as.factor(Patient_Sarcoma_data_filtered$anno)
DimPlot(Patient_Sarcoma_data_filtered, label = T, repel = T, group.by = 'anno')##Figure
tiff('../Figures/SFA_DSRCT_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
dev.off()
null device
1 ##Gene signatures
gene_sigs <- read.delim("../gene_signatures.txt")
gene_sigs <- as.list(gene_sigs)
gene_sigs <- gene_sigs[c("EWS.FLI1" ,"EWS.WT1", '')]
gene_sigs <- lapply(gene_sigs, function(x) gsub(" ", "", x))
signature_names <- names(gene_sigs)
#generate a rename vector to easily rename each of the signatures
rename_vector <- setNames( paste0('Cluster', 1:length(signature_names)), paste0(signature_names, '_Signature'))
#use AddModuleScore to assess the signature expression in each library
Patient_Sarcoma_data_filtered <- AddModuleScore(Patient_Sarcoma_data_filtered, features = gene_sigs, name = 'Cluster', ctrl = 50)
#DSRCT_multiome@meta.data <- DSRCT_multiome@meta.data %>% dplyr::select(-c(names(rename_vector)))
Patient_Sarcoma_data_filtered@meta.data <- Patient_Sarcoma_data_filtered@meta.data %>% dplyr::rename(rename_vector)signature_vln_plot_list <- VlnPlot(Patient_Sarcoma_data_filtered, group.by = 'anno',features = names(rename_vector)[2:1], combine = F)
signature_vln_plot_list <- lapply(signature_vln_plot_list, function(x) x +
theme(legend.position = 'none', axis.title.x = element_blank()) +
scale_fill_manual(values = cell_type_colors))
signature_vln_plot_list[[1]] <- signature_vln_plot_list[[1]] + ggtitle('EWS::WT1 Score')+ ylim(-0.2,0.4)Scale for y is already present.
Adding another scale for y, which will replace the existing scale.signature_vln_plot_list[[2]] <- signature_vln_plot_list[[2]] + ggtitle('EWS::FLI1 Score') + ylim(-0.2,0.4)Scale for y is already present.
Adding another scale for y, which will replace the existing scale.signature_vln_plot_list[[1]]
[[2]]
dsrct_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('DSRCT', rownames(Patient_Sarcoma_data_filtered))]
dsrct_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = dsrct_ng, group.by = 'anno') + theme(axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 7.5
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c() + labs(x='',y ='') + scale_color_viridis_c()
dsrct_ng_dpDotPlot(Patient_Sarcoma_data_filtered, features = dsrct_ng, group.by = 'seurat_clusters', cluster.idents = T) + theme(axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 7.5
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c() + labs(x='',y ='') + scale_color_viridis_c()
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
##SFA Figure
top <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1, labels = 'AUTO')
tiff('../Figures/SFA_DSRCT_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
dev.off()
tiff('../Figures/SFA_DSRCT_CDS_signature.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(top, dsrct_ng_dp, labels = c('', 'C'), nrow = 2, rel_heights = c(1, 0.8))
dev.off()##Manuscript Figure
vln_plots <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1)Warning: Removed 160 rows containing non-finite values (`stat_ydensity()`).Warning: Removed 160 rows containing missing values (`geom_point()`).top <- plot_grid(anno_umap, vln_plots, labels = c('C', 'D'), nrow = 1)
bot <- plot_grid(gene_dot_plot, dsrct_ng_dp, labels = c('E', 'F'), nrow = 2, axis = 'tblr', align = 'hv')
tiff('../Figures/Neogenes_DSRCT_Figure_1.tiff', width = 10, height = 10, res = 300, units = 'in')
plot_grid(top, bot, nrow = 2, rel_heights = c(1,2))
dev.off()null device
1 #Heatmap
require(ComplexHeatmap)
Loading required package: ComplexHeatmap
========================================
ComplexHeatmap version 2.12.1
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
If you use it in published research, please cite either one:
- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
genomic data. Bioinformatics 2016.
- Gu, Z. Complex Heatmap Visualization. iMeta 2022.
The new InteractiveComplexHeatmap package can directly export static
complex heatmaps into an interactive Shiny app with zero effort. Have a try!
This message can be suppressed by:
suppressPackageStartupMessages(library(ComplexHeatmap))
========================================##Set up neogenes
require(scales)
NG_feature_list <-
list(
DSRCT_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('DSRCT-NG', rownames(Patient_Sarcoma_data_filtered))],
ES_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('Ew-NG', rownames(Patient_Sarcoma_data_filtered))],
aRMS_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('aRMS-NG', rownames(Patient_Sarcoma_data_filtered))]
#CDS_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('CIC-NG', rownames(Patient_Sarcoma_data_filtered))]
)
NG_feature_list <- lapply(NG_feature_list, function(x) {data.frame(gene = x)})
NG_features <- do.call(rbind, NG_feature_list) #used for rowsplitting
NG_features$group <- sapply(strsplit(rownames(NG_features), '[.]'), '[[', 1)
rownames(NG_features) <- NG_features$gene
#reordering the feature groups
NG_features$group <- factor(NG_features$group, names(NG_feature_list))
#ensuring that genes are found in gene matrix
genes <- intersect(unlist(NG_feature_list), rownames(Patient_Sarcoma_data_filtered))
NG_features <- NG_features[genes,]##Acquire Data Matrix
Patient_Sarcoma_data_filtered@active.ident <- Patient_Sarcoma_data_filtered$anno
Patient_Sarcoma_data_filtered_subset <- subset(Patient_Sarcoma_data_filtered, subset = anno %in% c('DSRCT-1', 'DSRCT-2', 'DSRCT-4'),downsample = 2000)mat <- as.matrix(Patient_Sarcoma_data_filtered_subset@assays$RNA@data[NG_features$gene,])
#mat_scaled <- apply(mat, 1, function(x) rescale(x, c(0,1))) %>% t()##Set up Colors
require(paletteer)
cluster_type_colors <- cell_type_colors[1:3]
colors <- as.character(paletteer_dynamic("cartography::pastel.pal", length((names(NG_feature_list)))))
NG_type_colors <- setNames( colors,names(NG_feature_list))
annotation_color <- list(anno = cluster_type_colors,
group = NG_type_colors)Create heatmap
Patient_data_ng_mat_hm <- Heatmap(
mat,
col = f1,
cluster_columns = T,
cluster_rows = T,
cluster_row_slices = F,
top_annotation = c(ha_1),
left_annotation = ra_1,
show_row_names = T,
show_column_names = F,
heatmap_height = unit(8, 'in'),
heatmap_width = unit(8, 'in'),
column_names_rot = 90,
column_title_rot = 0,
column_title_gp = gpar(fontsize = 12),
column_split = Patient_Sarcoma_data_filtered_subset@meta.data[,'anno', drop = F],
row_split = NG_features[,'group', drop = F],
row_names_gp = gpar(fontsize = 8),
row_title_gp = gpar(fontsize = 12),
row_title_side = 'left',
row_title_rot = 0,
show_heatmap_legend = TRUE,
heatmap_legend_param = list(
title = 'Log Expression',
direction = "vertical",
legend_width = unit(3, 'cm')
)
)
`use_raster` is automatically set to TRUE for a matrix with more than 2000 columns You can control
`use_raster` argument by explicitly setting TRUE/FALSE to it.
Set `ht_opt$message = FALSE` to turn off this message.Draw Heatmap
Patient_data_ng_mat_hm_grob <- grid::grid.grabExpr(
ComplexHeatmap::draw(
Patient_data_ng_mat_hm,
merge_legend = T,
#annotation_legend_list = list(lgd1),
heatmap_legend_side = c("right"),
annotation_legend_side = c("right"),
align_heatmap_legend = 'heatmap_top'
)
)
plot_grid(Patient_data_ng_mat_hm_grob)
###Make Figure
tiff('../Figures/Neogenes_Supplemental.tiff', width = 10, height = 10, res = 300, units = 'in')
plot_grid(Patient_data_ng_mat_hm_grob)
dev.off()null device
1 ---
title: "R Notebook"
output: html_notebook
---

#Introduction
I am analyzing neogene expression in Patient sarcoma data.

#Load libraries
```{r}
library(Seurat)
library(scater)
library(readxl)
library(dplyr)
library(stringr)
library(tidyr)
library(plyr)
source('../../Utilities/Utilities.r')
source('../../Utilities/Processing_Utilities.r')
```

#importing data from file storage

Files are stored on my MD Anderson drive. I am extracting the the path to each library and generating some meta data.
```{r}
list.dirs('/Volumes/ludwig_lab/CellRanger/data_output/', recursive = F)
list_of_files <- path_input('/Volumes/ludwig_lab/CellRanger/data_output/', library.split = 7, sample.split = 1)
list_of_files <- list_of_files[c(3,5,7,10),]
```

#converting from H5 to seurat objects
```{r}
Patient_Sarcoma_neogenes.list.h5 <- lapply(list_of_files$h5, Read10X_h5)
names(Patient_Sarcoma_neogenes.list.h5) <- list_of_files$library_id
Patient_Sarcoma_neogenes.list <- lapply( Patient_Sarcoma_neogenes.list.h5 ,function(x) {CreateSeuratObject(counts = x) })
names(Patient_Sarcoma_neogenes.list) <- list_of_files$library_id

for(i in 1:length(Patient_Sarcoma_neogenes.list)) {
  Patient_Sarcoma_neogenes.list[[i]]<- RenameCells(object = Patient_Sarcoma_neogenes.list[[i]], new.names = paste0(
    sapply(strsplit(as.character(colnames(Patient_Sarcoma_neogenes.list[[i]])), split="-"), "[[", 1),
    "-", i))
}

Patient_Sarcoma_data  <- Reduce(merge, Patient_Sarcoma_neogenes.list)
```

#adding the metadata
```{r}
Patient_Sarcoma_data <- PercentageFeatureSet( Patient_Sarcoma_data, pattern = "^MT-", col.name = "percent.mt", assay = 'RNA')

gemgroup <- sapply(strsplit(rownames(Patient_Sarcoma_data@meta.data), split="-"), "[[", 2)
Patient_Sarcoma_data<- AddMetaData(object=Patient_Sarcoma_data, metadata=data.frame(gemgroup=gemgroup, row.names=rownames(Patient_Sarcoma_data@meta.data)))

Patient_Sarcoma_data$orig.ident <- mapvalues(Patient_Sarcoma_data$gemgroup,unique(Patient_Sarcoma_data$gemgroup), as.character(list_of_files$library_id))

Patient_Sarcoma_data$sample_type <-  mapvalues(Patient_Sarcoma_data$gemgroup, unique(Patient_Sarcoma_data$gemgroup), as.character(list_of_files$sample_type))
```

```{r}
Patient_Sarcoma_data$lab_id <- mapvalues(Patient_Sarcoma_data$orig.ident, unique(Patient_Sarcoma_data$orig.ident), c('DSRCT-1', 'DSRCT-4', 'DSRCT-2',
                                                                                             'CDS-1' ))

Patient_Sarcoma_data@meta.data %>% group_by(orig.ident, lab_id) %>% summarise(n=n())
```

#quality control
```{r}
RidgePlot(Patient_Sarcoma_data, features = 'nCount_RNA', group.by = 'lab_id') + scale_x_log10()
RidgePlot(Patient_Sarcoma_data, features = 'nFeature_RNA', group.by = 'lab_id')+ scale_x_log10()
RidgePlot(Patient_Sarcoma_data, features = 'percent.mt', group.by = 'lab_id')
Patient_Sarcoma_data
```

```{r}
Patient_Sarcoma_data <- readRDS('/Volumes/ludwig_lab/NeoGenes/RDS/Patient_Sarcoma_dataoriginal.rds')
```


#filter out low quality cells
```{r}
Patient_Sarcoma_data_filtered <- subset(Patient_Sarcoma_data, 
                                        subset = 
                                  percent.mt < 10 & nCount_RNA > 1000 & nFeature_RNA > 500)
Patient_Sarcoma_data
Patient_Sarcoma_data_filtered
```

#save original data
```{r}
saveRDS(Patient_Sarcoma_data, '/Volumes/ludwig_lab/NeoGenes/RDS/Patient_Sarcoma_dataoriginal.rds')
```

#Processing (including CDS)
```{r}
Patient_Sarcoma_data <- readRDS('/Volumes/ludwig_lab/NeoGenes/RDS/Patient_Sarcoma_dataoriginal.rds')
```

##normalize
```{r}
Patient_Sarcoma_data_filtered <- Patient_Sarcoma_data_filtered %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA()

ElbowPlot(Patient_Sarcoma_data_filtered)
```
##UMAP
```{r}
Patient_Sarcoma_data_filtered <- RunUMAP(Patient_Sarcoma_data_filtered, dims = 1:15)
```
## Lab ID
```{r}
Patient_Sarcoma_data_filtered@active.ident <- factor(Patient_Sarcoma_data_filtered$lab_id)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'lab_id', label = T)
```

## Genes
```{r}
FeaturePlot(Patient_Sarcoma_data_filtered, features = 'COL1A1')
FeaturePlot(Patient_Sarcoma_data_filtered, features = 'ETV1')
FeaturePlot(Patient_Sarcoma_data_filtered, features = 'ST6GALNAC5')
FeaturePlot(Patient_Sarcoma_data_filtered, features = 'CD163')
FeaturePlot(Patient_Sarcoma_data_filtered, features = 'PECAM1')
FeaturePlot(Patient_Sarcoma_data_filtered, features = 'CD8A')
```
## Find Neighbors
```{r}
Patient_Sarcoma_data_filtered <-  FindNeighbors(Patient_Sarcoma_data_filtered , dims=1:15) 
Patient_Sarcoma_data_filtered <- FindClusters(Patient_Sarcoma_data_filtered,res = 1 )
```
## Labeling Cells
```{r}
Patient_Sarcoma_data_filtered$anno <- Patient_Sarcoma_data_filtered$seurat_clusters
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(16)] <- 'Skeletal Muscle'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(15,24,20)] <- 'Fibroblasts'
levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(19, 23)] <- 'Immune Cells'

levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(22)] <- 'Endothelial Cells'

levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(10)] <- 'CDS-1'

levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(1, 11,3, 8,12,13)] <- 'DSRCT-4'

levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(2,4,7,9,18,14,17)] <- 'DSRCT-2'

levels(Patient_Sarcoma_data_filtered$anno)[levels(Patient_Sarcoma_data_filtered$anno) %in% c(21,0,5,6)] <- 'DSRCT-1'
```



##View UMAPs
```{r}
DimPlot(Patient_Sarcoma_data_filtered, label = T)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'lab_id', label = T)
```
```{r, fig.width=10}
classical_genes <- list(DSRCT = c( 'GAL', 'ST6GALNAC5', 
                                  'CACNA2D2' , 'KRT8', 'DES', 'NCAM1'),
                        CDS = c('ETV1', 'ETV4'),
                        Fibroblasts = c('COL1A1', 'COL1A2', 'FN1'),
                        'Skeletal\nmuscle' = c('TTN', 'MYH3', 'NEB'),
                        'Immune\ncells' = c('PTPRC', 'CD4', 'CD86'),
                        'Endothelial\ncells' = c('VWF', 'PECAM1','CDH5')
                        )


gene_dot_plot <-
  DotPlot(Patient_Sarcoma_data_filtered,
          features = classical_genes,
          group.by = 'anno') + theme(axis.text.x = element_text(
            angle = 45,
            hjust = 1,
            size = 10
          ),
          strip.text = element_text(size = 7.5)) + labs(x = '', y = '') + scale_color_viridis_c()
gene_dot_plot
```
###Figure
```{r, fig.width=8, fig.height=6}
anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T, repel = T) + ggtitle('') + theme(axis.text = element_blank())

tiff('../Figures/UO1_DSRCT_CDS_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
dev.off()
```


##Gene signatures
```{r}
gene_sigs <- read.delim("../gene_signatures.txt")
gene_sigs <- as.list(gene_sigs)
gene_sigs <- gene_sigs[c("EWS.FLI1" ,"EWS.WT1" ,"CIC.DUX4")]
gene_sigs <- lapply(gene_sigs, function(x) gsub(" ", "", x))

signature_names <- names(gene_sigs)

#generate a rename vector to easily rename each of the signatures
rename_vector <- setNames( paste0('Cluster', 1:length(signature_names)), paste0(signature_names, '_Signature'))

#use AddModuleScore to assess the signature expression in each library
Patient_Sarcoma_data_filtered <- AddModuleScore(Patient_Sarcoma_data_filtered, features = gene_sigs, name = 'Cluster', ctrl = 50)
#DSRCT_multiome@meta.data <- DSRCT_multiome@meta.data %>% dplyr::select(-c(names(rename_vector)))
Patient_Sarcoma_data_filtered@meta.data <- Patient_Sarcoma_data_filtered@meta.data %>% dplyr::rename(rename_vector)
```

```{r}

FeaturePlot(Patient_Sarcoma_data_filtered,features = 'CIC-NG6', order = T)
FeaturePlot(Patient_Sarcoma_data_filtered,features = names(rename_vector))

VlnPlot(Patient_Sarcoma_data_filtered, group.by = 'anno',features = names(rename_vector)[2:3], combine = F)
```

```{r}
signature_vln_plot_list <- VlnPlot(Patient_Sarcoma_data_filtered, group.by = 'anno',features = names(rename_vector)[2:3], combine = F)

signature_vln_plot_list <- lapply(signature_vln_plot_list, function(x) x + theme(legend.position = 'none',
                                                                                 axis.title.x = element_blank()))

signature_vln_plot_list[[1]] <- signature_vln_plot_list[[1]]  + ggtitle('EWS::WT1 Score')
signature_vln_plot_list[[2]]  <- signature_vln_plot_list[[2]] + ggtitle('CIC::DUX4 Score')
```

```{r, fig.width=10, fig.height=4}
cic_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('CIC-', rownames(Patient_Sarcoma_data_filtered))]
cic_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = cic_ng, group.by = 'anno') + theme(axis.text.x = element_text(
  angle = 45,
  hjust = 1,
  size = 10
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c()
cic_ng_dp
```


```{r, fig.width=10, fig.height=4}
dsrct_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('DSRCT', rownames(Patient_Sarcoma_data_filtered))]
dsrct_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = dsrct_ng, group.by = 'anno') + theme(axis.text.x = element_text(
  angle = 45,
  hjust = 1,
  size = 7.5
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c() + labs(x='',y ='') + scale_color_viridis_c()
dsrct_ng_dp
```
###Figure
```{r, fig.width=8, fig.height=6}
top <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1, labels = 'AUTO')

tiff('../Figures/UO1_DSRCT_CDS_signature.tiff', width = 8, height = 10, res = 300, units = 'in')
plot_grid(top, dsrct_ng_dp, cic_ng_dp, labels = c('', 'C', 'D'), nrow = 3, rel_heights = c(1, 0.8,0.8))
dev.off()
```


## DEGs
```{r}
Patient_Clusters_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 1000)
```
```{r}
saveRDS(Patient_Clusters_DEGs, '../RDS/2023-11-8 Patient_Clusters_DEGs.rds')
```



```{r}
top_Patient_Clusters_DEGs <- Patient_Clusters_DEGs %>% group_by(cluster) %>% top_n(10, avg_log2FC)
```

#
```{r}
cic_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('CIC-', rownames(Patient_Sarcoma_data_filtered))]
DotPlot(Patient_Sarcoma_data_filtered, features = cic_ng)
```



```{r}
DotPlot(Patient_Sarcoma_data_filtered, features = c('PTPRC', 'PECAM1', 'COL1A1', 'IL7R', 'CD163', 'CD8A', 'ACTA2', 'MYH3'))
```


#Save Data
```{r}
saveRDS(Patient_Sarcoma_data_filtered,'../RDS/2024-01-31_Patient_Sarcoma_data_filtered.rds')
```

#Split the data
```{r}
Patient_Sarcoma_data_filtered_list <- SplitObject(Patient_Sarcoma_data_filtered, split.by = 'lab_id')
```

```{r}
Patient_Sarcoma_data_filtered_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x){
  x %>% 
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA()
})
```

```{r}
lapply(Patient_Sarcoma_data_filtered_list,ElbowPlot)
```

```{r}
Patient_Sarcoma_data_filtered_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x){
  RunUMAP(x, dims = 1:15)
})
```
##Clusters
```{r}
Patient_Sarcoma_data_filtered_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x){
  x <-  FindNeighbors(x , dims=1:15) 
  x <- FindClusters(x )
})
```

```{r}
lapply(Patient_Sarcoma_data_filtered_list, function(x)
  DimPlot(x, label= T) + NoLegend())
```

```{r}
lapply(Patient_Sarcoma_data_filtered_list, function(x)
  FeaturePlot(x, 'WT1'))
```
##DEGs
```{r}
Patient_Clusters_DEG_list <- lapply(Patient_Sarcoma_data_filtered_list, function(x) FindAllMarkers(x, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 200))
```

```{r}
saveRDS(Patient_Clusters_DEG_list, '../RDS/2023-05-01-Patient_Clusters_DEG_list.rds')
```

```{r}
Patient_Clusters_DEG_list_top <- lapply(Patient_Clusters_DEG_list, function(x)
  x %>% group_by(cluster) %>% top_n(10, avg_log2FC))
```


##DotPlots
```{r, fig.width=6}
genes <- rownames(PDX_neogenes_filtered)[grep('DSRCT-NG', rownames(PDX_neogenes_filtered))]
lapply(Patient_Sarcoma_data_filtered_list,function(x) DotPlot(x, features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1)))
```

```{r, fig.width=6}
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1')

lapply(Patient_Sarcoma_data_filtered_list,function(x) DotPlot(x, features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1)))
```

```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-2` <- FindSubCluster(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, 
                                                               c('11'), 'RNA_snn')
```
###DSRCT-2
```{r}
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1', 'VWF', 'IL7R')

DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, group.by = 'sub.cluster', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))

DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, group.by = 'sub.cluster', label = T)
```


```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-2`$Anno <- Patient_Sarcoma_data_filtered_list$`DSRCT-2`$seurat_clusters

levels(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$Anno),'_',
                                                                    c(rep('DSRCT',10),
                                                                      'Fibroblasts',
                                                                      'Immune Cells'))

                                                                     

Patient_Sarcoma_data_filtered_list$`DSRCT-2`$cell_type <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$seurat_clusters)

levels(Patient_Sarcoma_data_filtered_list$`DSRCT-2`$cell_type) <-c(rep('DSRCT',10),
                                                                      'Fibroblasts',
                                                                      'Immune Cells')


DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, group.by = 'cell_type', label = T)
```
```{r, fig.width=10}
genes <- rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-2`)[grep('DSRCT-NG', rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-2`))]
DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-2`, features = genes, group.by = 'Anno') + theme(axis.text.x = element_text(angle =45, hjust=1))
```

###DSRCT-4
```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-4` <- FindSubCluster(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, 
                                                               c('11'), 'RNA_snn')
```

```{r}
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1', 'VWF', 'FAP', 'WT1')

DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'sub.cluster', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
```

```{r}
genes <- c('DSRCT-NG2', 'DSRCT-NG6', 'MKI67', 'ZEB1', 'WT1','PDGFRA', 
           'COL1A1', 'ACTA2', 'PECAM1',  'IL7R',   'CD163', 'CD68', 'VWF')

DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'Anno', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
```

```{r}
genes <- rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-4`)[grep('DSRCT-NG', rownames(Patient_Sarcoma_data_filtered_list$`DSRCT-4`))]

DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'Anno', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
```

```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno <- Patient_Sarcoma_data_filtered_list$`DSRCT-4`$seurat_clusters

levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno),'_',
                                                                    c(rep('DSRCT',11),
                                                                      'Fibroblasts', #Mesothelial Cells
                                                                      'Immune Cells', #Myeloid Cells
                                                                      'Immune Cells')) #T-Cells

                                                                     

Patient_Sarcoma_data_filtered_list$`DSRCT-4`$cell_type <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$seurat_clusters)

levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$cell_type) <- c(rep('DSRCT',11),
                                                                      'Fibroblasts', #Mesothelial Cells
                                                                      'Immune Cells', #Myeloid Cells
                                                                      'Immune Cells') #T-Cells

DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'cell_type', label = T)
```


###DSRCT-1
```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-1`@active.ident <-factor( Patient_Sarcoma_data_filtered_list$`DSRCT-1`$seurat_clusters)
Patient_Sarcoma_data_filtered_list$`DSRCT-1` <- FindSubCluster(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, 
                                                               10, 'RNA_snn', res = 1)

DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'sub.cluster', label = T )
```

```{r}
genes <- c('DSRCT-NG2', 'PTPRC', 'COL1A1', 'ACTA2', 'PECAM1', 'VWF', 'IL7R', 'CD68')

DotPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'sub.cluster', features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
```

```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-1`@active.ident <-factor( Patient_Sarcoma_data_filtered_list$`DSRCT-1`$sub.cluster)

Patient_Sarcoma_data_filtered_list$`DSRCT-1`$Anno <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$sub.cluster)



levels(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$Anno),'_',
                                                                    c(rep('DSRCT',2),
                                                                      'Fibroblasts',
                                                                      'Neuronal', 
                                                                      'Immune Cells',
                                                                      rep('DSRCT',8)))

Patient_Sarcoma_data_filtered_list$`DSRCT-1`$cell_type <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$sub.cluster)

levels(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$cell_type) <-  c(rep('DSRCT',2),
                                                                      'Fibroblasts',
                                                                      'Immune Cells', 
                                                                      'Neuronal Cells',
                                                                      rep('DSRCT',8))

DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'cell_type', label = T)
```

```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-1`@active.ident <- factor(Patient_Sarcoma_data_filtered_list$`DSRCT-1`$cell_type)
DSRCT_1_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, group.by = 'cell_type',
                               only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 200)
```


```{r}
FeaturePlot(Patient_Sarcoma_data_filtered_list$`DSRCT-1`, features = 'NEUROD1')
```
```{r}
Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno <- Patient_Sarcoma_data_filtered_list$`DSRCT-1`$seurat_clusters

levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno) <- paste0(levels(Patient_Sarcoma_data_filtered_list$`DSRCT-4`$Anno),'_',
                                                                    c(rep('DSRCT',11),
                                                                      'Mesothelial Cells',
                                                                      'Myeloid Cells',
                                                                      'T-Cells'))

DimPlot(Patient_Sarcoma_data_filtered_list$`DSRCT-4`, group.by = 'Anno', label = T)
```

```{r}
lapply(Patient_Sarcoma_data_filtered_list[1:3], function(x) DimPlot(x, group.by = 'Anno', label = T))
```

#Extracting DSRCT data from PDX and patients 
Patient data will be exclusively from nuclei data
```{r}
DSRCT_only_cells <- lapply(Patient_Sarcoma_data_filtered_list[1:3], function(x) x[,x$cell_type == 'DSRCT'] )
DSRCT_only_cells_dataset <- merge(DSRCT_only_cells[[1]], DSRCT_only_cells[2:3])
DSRCT_only_cells_dataset$source = 'Patient'
```

```{r}
DSRCT_PDX_cells_dataset <- PDX_neogenes_filtered[,PDX_neogenes_filtered$lab_id %in% c('DSRCT-1', 'DSRCT-2', 'DSRCT-4')]

DSRCT_PDX_cells_dataset$source = 'PDX'
```

```{r}
DSRCT_dataset <- merge(DSRCT_only_cells_dataset, DSRCT_PDX_cells_dataset)
```

```{r}
DSRCT_dataset$library <- paste0(DSRCT_dataset$lab_id, '_', DSRCT_dataset$source)
DSRCT_dataset@active.ident <- factor(DSRCT_dataset$library)
```

```{r, fig.width=10}
genes <- rownames(DSRCT_dataset)[grep('DSRCT-NG', rownames(DSRCT_dataset))]
DotPlot(DSRCT_dataset, features = genes) + theme(axis.text.x = element_text(angle =45, hjust=1))
```

```{r}
DSRCT_dataset_avg <- AverageExpression(DSRCT_dataset)
```

```{r}
genes <- rownames(DSRCT_dataset)[grep('-NG', rownames(DSRCT_dataset))]
mat <- DSRCT_dataset_avg$RNA[genes,]
pheatmap::pheatmap(cor(mat))
```

```{r}
require(ggrepel)
pca <- prcomp(t(mat))
pca_df <- data.frame(PC_1 = pca$x[,1], PC_2 = pca$x[,2], labels = rownames(pca$x))

ggplot(pca_df, aes(x = PC_1, y = PC_2, label = labels)) + 
  geom_text_repel() + 
  labs(x= paste0('PC_1 ', '(',round(pca$sdev[1],2), '%)'),
       y= paste0('PC_2 ', '(',round(pca$sdev[2],2), '%)'))
```



#Processing (excluding CDS)
```{r}
Patient_Sarcoma_data <- readRDS('/Volumes/ludwig_lab/NeoGenes/RDS/Patient_Sarcoma_dataoriginal.rds')
Patient_Sarcoma_data <- Patient_Sarcoma_data[,grep('DSRCT', Patient_Sarcoma_data$lab_id)]
```

##filter out low quality cells
```{r}
Patient_Sarcoma_data_filtered <- subset(Patient_Sarcoma_data, 
                                        subset = 
                                  percent.mt < 10 & nCount_RNA > 1000 & nFeature_RNA > 500)
Patient_Sarcoma_data
Patient_Sarcoma_data_filtered
```

##normalize
```{r}
Patient_Sarcoma_data_filtered <- Patient_Sarcoma_data_filtered %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA()

ElbowPlot(Patient_Sarcoma_data_filtered)
```

##UMAP
```{r}
Patient_Sarcoma_data_filtered <- RunUMAP(Patient_Sarcoma_data_filtered, dims = 1:50)
```
## Lab ID
```{r}
Patient_Sarcoma_data_filtered@active.ident <- factor(Patient_Sarcoma_data_filtered$lab_id)
DimPlot(Patient_Sarcoma_data_filtered, group.by = 'lab_id', label = T)
```

## Find Neighbors
```{r}
Patient_Sarcoma_data_filtered <-  FindNeighbors(Patient_Sarcoma_data_filtered , dims=1:50) 
Patient_Sarcoma_data_filtered <- FindClusters(Patient_Sarcoma_data_filtered,res = 0.3)
DimPlot(Patient_Sarcoma_data_filtered, label = T, group.by = 'seurat_clusters')
FeaturePlot(Patient_Sarcoma_data_filtered, 'DSRCT-NG13')
```
```{r}
Patient_Sarcoma_data_filtered$anno <- Patient_Sarcoma_data_filtered$seurat_clusters
levels(Patient_Sarcoma_data_filtered$anno) <- c('DSRCT-1', 'DSRCT-2', 'DSRCT-4', 'DSRCT-4' ,'DSRCT-2', 'DSRCT-4', 'DSRCT-4', 'DSRCT-4', 'DSRCT-2',
                                                '9', '10', 'DSRCT-1', 'DSRCT-4', 'DSRCT-2')
DimPlot(Patient_Sarcoma_data_filtered, label = T, group.by = 'anno')
```


## DEGs
```{r}
Patient_Clusters_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 500)
```

```{r}
Patient_Sarcoma_data_filtered_subset <- subset(Patient_Sarcoma_data_filtered, subset = seurat_clusters %in% c('9','10'))
```

```{r}
Patient_Sarcoma_data_filtered_subset <- FindNeighbors(Patient_Sarcoma_data_filtered_subset, dims = 1:50)
Patient_Sarcoma_data_filtered_subset <- FindClusters(Patient_Sarcoma_data_filtered_subset)
```

```{r}
Patient_Sarcoma_data_filtered_subset_Clusters_DEGs <- FindAllMarkers(Patient_Sarcoma_data_filtered_subset, only.pos = T, test.use = 'LR', return.thresh = 0.05, max.cells.per.ident = 500)
```

```{r}
DimPlot(Patient_Sarcoma_data_filtered_subset, label = T)
```
```{r}
Patient_Sarcoma_data_filtered_subset$anno <- Patient_Sarcoma_data_filtered_subset$seurat_clusters
levels(Patient_Sarcoma_data_filtered_subset$anno) <- c('Fibroblasts', 'Fibroblasts', 'T-cells', 
                                                       'Endothelial cells', 'Myeloid cells', 'Myeloid cells',
                                                       'Fibroblasts', 'DSRCT-2', 'Myeloid cells')
```




```{r}
Patient_Sarcoma_data_filtered$anno <- Patient_Sarcoma_data_filtered$seurat_clusters
levels(Patient_Sarcoma_data_filtered$anno) <- c('DSRCT-1', 'DSRCT-2', 'DSRCT-4', 'DSRCT-4' ,'DSRCT-2', 'DSRCT-4', 'DSRCT-4', 'DSRCT-4', 'DSRCT-2',
                                                '9', '10', 'DSRCT-1', 'DSRCT-4', 'DSRCT-2')

Patient_Sarcoma_data_filtered$anno <- as.character(Patient_Sarcoma_data_filtered$anno)
Patient_Sarcoma_data_filtered@meta.data[colnames(Patient_Sarcoma_data_filtered_subset),'anno'] <- as.character(Patient_Sarcoma_data_filtered_subset$anno)
Patient_Sarcoma_data_filtered$anno <- as.factor(Patient_Sarcoma_data_filtered$anno)

DimPlot(Patient_Sarcoma_data_filtered, label = T, repel = T, group.by = 'anno')
```

```{r}
colors <- as.character(paletteer_dynamic("cartography::multi.pal", length(unique(Patient_Sarcoma_data_filtered$anno))))
cell_type_colors <- setNames(colors,levels(Patient_Sarcoma_data_filtered$anno))
```


##Figure
```{r, fig.width=8}
classical_genes <- list(DSRCT = c( 'GAL', 'ST6GALNAC5', 
                                  'CACNA2D2' , 'KRT8', 'DES', 'NCAM1'),
                         'Endothelial\ncells' = c('VWF', 'CDH5'),
                        Fibroblasts = c('COL1A1', 'COL1A2', 'FN1'),
                        'Myeloid\ncells' = c( 'CD163', 'CD86','PTPRC'),
                        'T-cells' = c( 'IL7R','CD3E', 'CD8A')
                       )
gene_dot_plot <-
  DotPlot(Patient_Sarcoma_data_filtered,
          features = classical_genes, group.by = 'anno') + theme(axis.text.x = element_text(
            angle = 45,
            hjust = 1,
            size = 10
          ),
          legend.title  = element_text(size = 10),
          legend.text = element_text(size = 10),
          strip.text = element_text(size = 7)) + labs(x = '', y = '') + scale_color_viridis_c()
gene_dot_plot
```

```{r, fig.width=8, fig.height=6}
anno_umap <- DimPlot(Patient_Sarcoma_data_filtered, group.by = 'anno', label = T, repel = T) + ggtitle('') + theme(axis.text = element_blank()) + scale_color_manual(values = cell_type_colors)
```

##Gene signatures
```{r}
gene_sigs <- read.delim("../gene_signatures.txt")
gene_sigs <- as.list(gene_sigs)
gene_sigs <- gene_sigs[c("EWS.FLI1" ,"EWS.WT1", '')]
gene_sigs <- lapply(gene_sigs, function(x) gsub(" ", "", x))

signature_names <- names(gene_sigs)

#generate a rename vector to easily rename each of the signatures
rename_vector <- setNames( paste0('Cluster', 1:length(signature_names)), paste0(signature_names, '_Signature'))

#use AddModuleScore to assess the signature expression in each library
Patient_Sarcoma_data_filtered <- AddModuleScore(Patient_Sarcoma_data_filtered, features = gene_sigs, name = 'Cluster', ctrl = 50)
#DSRCT_multiome@meta.data <- DSRCT_multiome@meta.data %>% dplyr::select(-c(names(rename_vector)))
Patient_Sarcoma_data_filtered@meta.data <- Patient_Sarcoma_data_filtered@meta.data %>% dplyr::rename(rename_vector)
```

```{r}
signature_vln_plot_list <- VlnPlot(Patient_Sarcoma_data_filtered, group.by = 'anno',features = names(rename_vector)[2:1], combine = F)

signature_vln_plot_list <- lapply(signature_vln_plot_list, function(x) x + 
                                    theme(legend.position = 'none', axis.title.x = element_blank()) +
                                    scale_fill_manual(values = cell_type_colors))

signature_vln_plot_list[[1]] <- signature_vln_plot_list[[1]]  + ggtitle('EWS::WT1 Score')+ ylim(-0.2,0.4)
signature_vln_plot_list[[2]]  <- signature_vln_plot_list[[2]] + ggtitle('EWS::FLI1 Score') + ylim(-0.2,0.4)
signature_vln_plot_list
```

```{r, fig.width=10, fig.height=4}
dsrct_ng <- rownames(Patient_Sarcoma_data_filtered)[grep('DSRCT', rownames(Patient_Sarcoma_data_filtered))]
dsrct_ng_dp <- DotPlot(Patient_Sarcoma_data_filtered, features = dsrct_ng, group.by = 'anno') + theme(axis.text.x = element_text(
  angle = 45,
  hjust = 1,
  size = 7.5
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c() + labs(x='',y ='') + scale_color_viridis_c()
dsrct_ng_dp
```

```{r}
DotPlot(Patient_Sarcoma_data_filtered, features = dsrct_ng, group.by = 'seurat_clusters', cluster.idents = T) + theme(axis.text.x = element_text(
  angle = 45,
  hjust = 1,
  size = 7.5
), legend.title = element_text(size = 10), legend.text = element_text(size = 10)) + labs(x = '', y = '') + scale_color_viridis_c() + labs(x='',y ='') + scale_color_viridis_c()
```


##SFA Figure
```{r, fig.width=8, fig.height=6}
top <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1, labels = 'AUTO')

tiff('../Figures/SFA_DSRCT_UMAP.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(anno_umap, gene_dot_plot, labels = 'AUTO', nrow = 2)
dev.off()

tiff('../Figures/SFA_DSRCT_CDS_signature.tiff', width = 8, height = 6, res = 300, units = 'in')
plot_grid(top, dsrct_ng_dp, labels = c('', 'C'), nrow = 2, rel_heights = c(1, 0.8))
dev.off()
```

##Manuscript Figure
```{r, fig.width=8, fig.height=6}
vln_plots <- plot_grid(plotlist = signature_vln_plot_list, nrow = 1)
top <- plot_grid(anno_umap, vln_plots, labels = c('C', 'D'), nrow = 1)
bot <- plot_grid(gene_dot_plot,  dsrct_ng_dp,  labels = c('E', 'F'), nrow = 2, axis = 'tblr', align = 'hv')
tiff('../Figures/Neogenes_DSRCT_Figure_1.tiff', width = 10, height = 10, res = 300, units = 'in')
plot_grid(top, bot, nrow = 2, rel_heights = c(1,2))
dev.off()
```

#Heatmap
```{r}
require(ComplexHeatmap)
```
##Set up neogenes
```{r}
require(scales)
NG_feature_list <- 
  list(
    DSRCT_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('DSRCT-NG', rownames(Patient_Sarcoma_data_filtered))], 
    ES_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('Ew-NG', rownames(Patient_Sarcoma_data_filtered))], 
    aRMS_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('aRMS-NG', rownames(Patient_Sarcoma_data_filtered))]
    #CDS_NGs = rownames(Patient_Sarcoma_data_filtered)[grep('CIC-NG', rownames(Patient_Sarcoma_data_filtered))]
  )

NG_feature_list <- lapply(NG_feature_list, function(x) {data.frame(gene = x)})
NG_features <- do.call(rbind, NG_feature_list) #used for rowsplitting
NG_features$group <- sapply(strsplit(rownames(NG_features), '[.]'), '[[', 1)
rownames(NG_features) <- NG_features$gene
#reordering the feature groups
NG_features$group <- factor(NG_features$group, names(NG_feature_list))

#ensuring that genes are found in gene matrix
genes <- intersect(unlist(NG_feature_list), rownames(Patient_Sarcoma_data_filtered))

NG_features <- NG_features[genes,]
```

##Acquire Data Matrix
```{r}
Patient_Sarcoma_data_filtered@active.ident <- Patient_Sarcoma_data_filtered$anno
Patient_Sarcoma_data_filtered_subset <- subset(Patient_Sarcoma_data_filtered, subset = anno %in% c('DSRCT-1', 'DSRCT-2', 'DSRCT-4'),downsample = 2000)
```

```{r}
mat <- as.matrix(Patient_Sarcoma_data_filtered_subset@assays$RNA@data[NG_features$gene,])
#mat_scaled <- apply(mat, 1, function(x) rescale(x, c(0,1))) %>% t()
```

##Set up Colors
```{r}
require(paletteer)
cluster_type_colors <- cell_type_colors[1:3]

colors <- as.character(paletteer_dynamic("cartography::pastel.pal", length((names(NG_feature_list)))))
NG_type_colors <- setNames( colors,names(NG_feature_list))

annotation_color <- list(anno = cluster_type_colors,
                         group = NG_type_colors)
```

## Create heatmap
```{r}
#color function for the scale
f1 = colorRamp2(c(0, 6),
                c("white", "darkblue"),
                transparency = 0,
                space = "LAB")

ha_1 = HeatmapAnnotation(
  df =  Patient_Sarcoma_data_filtered_subset@meta.data[, 'anno', drop = FALSE],
  col = annotation_color,
  annotation_label = 'Annotation',
  annotation_name_gp = gpar(fontsize = 12),
  border = TRUE,
  show_legend = F,
  simple_anno_size  = unit(0.35, 'cm'),
  annotation_legend_param = list(anno = list(
    ncol = 1, title_position = "topleft"
  ))
)

ra_1 = rowAnnotation(
  df =  NG_features[, 'group', drop = FALSE],
  col = annotation_color,
  annotation_label = 'Neogenes',
  annotation_name_gp = gpar(fontsize = 12),
  border = TRUE,
  show_legend = F,
  simple_anno_size  = unit(0.35, 'cm'),
  annotation_legend_param = list(group = list(
    nrow = 1, title_position = "topleft"
  ))
)

Patient_data_ng_mat_hm <- Heatmap(
  mat,
  col = f1,
  cluster_columns = T,
  cluster_rows = T,
  cluster_row_slices = F,
  top_annotation =  c(ha_1),
  left_annotation = ra_1,
  show_row_names = T,
  show_column_names = F,
  heatmap_height = unit(8, 'in'),
  heatmap_width = unit(8, 'in'),
  column_names_rot = 90,
  column_title_rot = 0,
  column_title_gp = gpar(fontsize = 12),
  column_split = Patient_Sarcoma_data_filtered_subset@meta.data[,'anno', drop = F],
  row_split = NG_features[,'group', drop = F],
  row_names_gp = gpar(fontsize = 8),
  row_title_gp = gpar(fontsize = 12),
  row_title_side = 'left',
  row_title_rot = 0,
  show_heatmap_legend = TRUE,
  heatmap_legend_param = list(
    title = 'Log Expression',
    direction = "vertical",
    legend_width = unit(3, 'cm')
  )
)
```
## Draw Heatmap
```{r, fig.height=10}
Patient_data_ng_mat_hm_grob <- grid::grid.grabExpr(
  ComplexHeatmap::draw(
    Patient_data_ng_mat_hm,
    merge_legend = T,
    #annotation_legend_list = list(lgd1),
    heatmap_legend_side = c("right"),
    annotation_legend_side = c("right"),
    align_heatmap_legend = 'heatmap_top'
  )
)

plot_grid(Patient_data_ng_mat_hm_grob)
```

###Make Figure
```{r}
tiff('../Figures/Neogenes_Supplemental.tiff', width = 10, height = 10, res = 300, units = 'in')
plot_grid(Patient_data_ng_mat_hm_grob)
dev.off()
```


